From 842f125ac07f12cf844bb530b1e3755ea65af7da Mon Sep 17 00:00:00 2001 From: Jammy2211 Date: Thu, 24 Sep 2026 14:03:31 +0200 Subject: [PATCH 1/6] profiling(point_source): static step-0 lattice A/B cell + RAL CPU/A100 submits (PyAutoArray#568 phase 3) scripts/point_source/likelihood_breakdown/static_lattice_ab.py: interleaved in-process A/B of AbstractSolver._initial_triangles with four routes -- control (flat 3N table, 69 849 rows), exact (28 665 exact-float rows), lattice (11 859 geometric vertices), library (self-labels from the traced step-0 deflection row count). Fresh closures + jax.clear_caches(); 20 x 20 default protocol; records FLOPs, memory_analysis, RSS delta, compile/lower/ first-call, table build + cache-hit time, observed XLA thread count and affinity. Tolerance gates (log L rel 1e-12, image counts + positions 1e-10, non-zero grad, vmap, step-0 containing sets). --constant-folding re-enables folding in a separate process via a dummy pass name and refuses (CPU) unless an HLO probe confirms folding ran. Submits: RAL CPU pinned to euclid-ral-compute-1 (phase-2 EPYC host), folding off + on, branch clones of PyAutoArray/PyAutoLens with import-origin refusal; A100 regression leg. Co-Authored-By: Claude Opus 5.5 --- ...oint_source_static_lattice_ab_ral_cpu_fp64 | 165 ++ ...n_point_source_static_lattice_ab_a100_fp64 | 125 ++ .../likelihood_breakdown/static_lattice_ab.py | 1577 +++++++++++++++++ 3 files changed, 1867 insertions(+) create mode 100755 hpc/batch_cpu/submit_breakdown_point_source_static_lattice_ab_ral_cpu_fp64 create mode 100755 hpc/batch_gpu/submit_breakdown_point_source_static_lattice_ab_a100_fp64 create mode 100644 scripts/point_source/likelihood_breakdown/static_lattice_ab.py diff --git a/hpc/batch_cpu/submit_breakdown_point_source_static_lattice_ab_ral_cpu_fp64 b/hpc/batch_cpu/submit_breakdown_point_source_static_lattice_ab_ral_cpu_fp64 new file mode 100755 index 000000000..cab0fc2e5 --- /dev/null +++ b/hpc/batch_cpu/submit_breakdown_point_source_static_lattice_ab_ral_cpu_fp64 @@ -0,0 +1,165 @@ +#!/bin/bash -l +# +# 2026-09 point-source CPU speed-up campaign, phase 3: the static step-0 +# lattice A/B (PyAutoArray#568). +# +# WHAT THIS MEASURES. One interleaved, in-process A/B of the JAX PointSolver's +# step-0 tiling (AbstractSolver._initial_triangles) inside the production +# point-source likelihoods, on ONE pinned, quiet RAL CPU node: +# +# scripts/point_source/likelihood_breakdown/static_lattice_ab.py +# control = the flat (3N, 2) per-triangle table (phase-2 main path; +# 69 849 step-0 rows for the simple lattice) -- the IN-JOB control; +# exact = the 28 665 exact-float distinct rows + inverse map (cell-built); +# lattice = the 11 859 geometric lattice vertices (cell-built, same +# arithmetic as the library's static_vertex_table); +# library = no substitution -- the imported PyAutoLens/PyAutoArray. HERE +# those are the FEATURE BRANCH clones (see LIBRARY below), so it +# must self-label library_matches = lattice. +# Rows: simple solved + plain + vmap-4 of solved; cluster solved + plain. +# Protocol: 20 rounds x 20 calls, route order rotated each round. +# Gates (tolerance, recorded in gate_summary): log L rel <= 1e-12, image +# counts identical + positions <= 1e-10, simple-solved grad allclose 1e-10, +# finite and NON-zero, vmap-vs-scalar rtol 1e-12, simple-plain step-0 +# containing_indices sets identical. +# +# TWO RUNS, TWO PROCESSES. (1) constant folding DISABLED (the flags every PyAuto +# submit uses); (2) --constant-folding: the cell swaps the disable flag for the +# dummy pass name constant_folding_probe_disabled before jax is imported +# (PyAutoNerves re-adds the real flag only when the substring is missing), and +# refuses to run unless its HLO probe shows that folding actually ran. +# +# HOST PINNING. Phase 1 landed on euclid-ral-compute-10-2 (Xeon 8490H) and +# phase 2 on euclid-ral-compute-1 (EPYC 7763); cluster control differed 15-20 % +# between them (host, not drift). This job is pinned to the phase-2 host with +# --nodelist so phase-2 and phase-3 numbers share a CPU; the control route is +# measured in the same job regardless, and only in-job ratios are quoted. +# If euclid-ral-compute-1 is down or saturated (sinfo -p ral -N -o "%N %t %C"), +# edit --nodelist to another node and say so in the campaign note. +# +# LIBRARY. The shared /mnt/ral/jnightin/PyAuto mirror is main. This submit +# PREPENDS git clones of PyAutoArray and PyAutoLens feature/point-source-cpu-p3 +# to PYTHONPATH. Prepare them once: +# +# for r in PyAutoArray PyAutoLens; do +# git clone --shared /mnt/ral/jnightin/PyAuto/$r /mnt/ral/jnightin/autolens_profiling_wt/${r}_point-source-cpu-p3 +# git -C /mnt/ral/jnightin/autolens_profiling_wt/${r}_point-source-cpu-p3 fetch \ +# https://github.com/PyAutoLabs/$r.git feature/point-source-cpu-p3 +# git -C /mnt/ral/jnightin/autolens_profiling_wt/${r}_point-source-cpu-p3 checkout --detach FETCH_HEAD +# done +# +# The job refuses to run unless autoarray AND autolens import from those clones; +# the JSON's source_revisions record their HEADs. +# +# THREADS. OMP/MKL/OPENBLAS pinned to 1 (BLAS family only); the JAX CPU pool +# sizes from the 8 CPUs of --cpus-per-task=8. The JSON records thread_environment, +# the observed process thread count after the first compile, sched_affinity, +# os_cpu_count and loadavg at start and end. +# +# Writes results/breakdown/point_source/static_lattice_ab_hpc_ral_cpu_fp64.{json,png} +# and static_lattice_ab_constant_folding_hpc_ral_cpu_fp64.{json,png}. +# +# WALL-BASIS: +# Laptop witness (WSL2, 8 cores, loaded, 2026-09-24), this cell at 20 x 20, simple + +# cluster: 975 s wall folding off (static_lattice_ab_laptop_cpu_fp64, first run) and +# 1571 s folding on (static_lattice_ab_constant_folding_laptop_cpu_fp64; the cluster +# plain control compile alone took 555 s with folding on). This job runs both: 2546 s, +# x 2.0 headroom = 5092 s, inside the 3-hour request. +# cell: point_source/static_lattice_ab/simple device: cpu precision: fp64 +# source: measured-wall wall: 2546 ref: laptop-point_source-static_lattice_ab_laptop_cpu_fp64 headroom: 2.0 + +#SBATCH -J static_lattice_ab +#SBATCH --partition=ral +#SBATCH --nodelist=euclid-ral-compute-1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=8 +#SBATCH --mem=32gb +#SBATCH --time=3:00:00 +#SBATCH -o output/output.%A.out +#SBATCH -e error/error.%A.err +#SBATCH --mail-type=END,FAIL +#SBATCH --mail-user=james.w.nightingale@durham.ac.uk + +# Repo root from where this was submitted (hpc/batch_cpu/../..), so it runs from +# a worktree of the feature branch. sbatch from inside hpc/batch_cpu/. +export AP_ROOT="$(cd "${SLURM_SUBMIT_DIR:-$(dirname "$0")}/../.." && pwd)" +echo "AP_ROOT=$AP_ROOT" +source $AP_ROOT/activate.sh + +export AA_BRANCH_SRC="${AA_BRANCH_SRC:-/mnt/ral/jnightin/autolens_profiling_wt/PyAutoArray_point-source-cpu-p3}" +export AL_BRANCH_SRC="${AL_BRANCH_SRC:-/mnt/ral/jnightin/autolens_profiling_wt/PyAutoLens_point-source-cpu-p3}" +for src in "$AA_BRANCH_SRC/autoarray" "$AL_BRANCH_SRC/autolens"; do + if [ ! -f "$src/__init__.py" ]; then + echo "FATAL: $src is not a library checkout (see LIBRARY in the header)" >&2 + exit 2 + fi +done +export PYTHONPATH="$AA_BRANCH_SRC:$AL_BRANCH_SRC:$PYTHONPATH" + +export OMP_NUM_THREADS=1 +export MKL_NUM_THREADS=1 +export OPENBLAS_NUM_THREADS=1 +export NPROC=${SLURM_CPUS_PER_TASK:-8} +export JAX_PLATFORMS=cpu +export JAX_ENABLE_X64=1 +export XLA_FLAGS="--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" +export NUMBA_CACHE_DIR=$AP_ROOT/output/numba_cache_${SLURM_JOB_ID:-local} +export MPLCONFIGDIR=/tmp/matplotlib_${SLURM_JOB_ID:-local} +mkdir -p "$NUMBA_CACHE_DIR" "$MPLCONFIGDIR" +# NO persistent JAX compile cache: the two runs differ only in XLA_FLAGS, and +# compile_s is a gate (+20 % stop rule), so every compile must be cold. +unset JAX_COMPILATION_CACHE_DIR + +echo "==========================================" +date +hostname +lscpu | grep 'Model name' || true +echo "partition: ${SLURM_JOB_PARTITION:-unset}" +echo "nodelist: ${SLURM_JOB_NODELIST:-unset}" +echo "cpus-per-task: ${SLURM_CPUS_PER_TASK:-unset}" +echo "sched_affinity: $(python3 -c 'import os; print(len(os.sched_getaffinity(0)))')" +echo "loadavg: $(cat /proc/loadavg)" +echo "Cell: point_source/likelihood_breakdown/static_lattice_ab" +echo "Precision: fp64 (JAX_ENABLE_X64=$JAX_ENABLE_X64)" +echo "Thread env: OMP=$OMP_NUM_THREADS MKL=$MKL_NUM_THREADS OPENBLAS=$OPENBLAS_NUM_THREADS NPROC=$NPROC" +echo "XLA_FLAGS: $XLA_FLAGS" + +echo "=== source revisions ===" +for r in PyAutoNerves PyAutoFit PyAutoArray PyAutoGalaxy PyAutoLens; do + printf '%-14s %s\n' "$r (shared)" "$(git -C /mnt/ral/jnightin/PyAuto/$r rev-parse HEAD 2>&1)" +done +echo "PyAutoArray (branch clone, IMPORTED): $(git -C $AA_BRANCH_SRC rev-parse HEAD 2>&1)" +echo "PyAutoLens (branch clone, IMPORTED): $(git -C $AL_BRANCH_SRC rev-parse HEAD 2>&1)" +echo "AP_ROOT rev: $(git -C $AP_ROOT rev-parse HEAD 2>&1)" +echo "AP_ROOT branch: $(git -C $AP_ROOT rev-parse --abbrev-ref HEAD 2>&1)" + +cd $AP_ROOT + +echo "=== import provenance (refuses unless both libraries come from the branch clones) ===" +python3 -c " +import os, autoarray, autolens, autofit, autogalaxy, jax +for m in (autoarray, autolens, autofit, autogalaxy): + print(f'{m.__name__}.__file__ =', m.__file__) +print('jax', jax.__version__, jax.devices()) +assert os.path.realpath(autoarray.__file__).startswith(os.path.realpath(os.environ['AA_BRANCH_SRC'])), 'autoarray not imported from the branch clone' +assert os.path.realpath(autolens.__file__).startswith(os.path.realpath(os.environ['AL_BRANCH_SRC'])), 'autolens not imported from the branch clone' +from autoarray.structures.triangles.coordinate_array import static_vertex_table +print('ASSERT OK: autoarray + autolens from the branch clones; static_vertex_table present') +" || exit 3 + +echo "==========================================" +echo "run 1/2: constant folding DISABLED (20 x 20, simple + cluster)" +date +echo "loadavg: $(cat /proc/loadavg)" +python3 -u scripts/point_source/likelihood_breakdown/static_lattice_ab.py --config-name hpc_ral_cpu_fp64 --rounds 20 --calls 20 + +echo "==========================================" +echo "run 2/2: constant folding ENABLED, separate process (20 x 20, simple + cluster)" +date +echo "loadavg: $(cat /proc/loadavg)" +python3 -u scripts/point_source/likelihood_breakdown/static_lattice_ab.py --config-name hpc_ral_cpu_fp64 --rounds 20 --calls 20 --constant-folding + +echo "==========================================" +echo "loadavg: $(cat /proc/loadavg)" +echo "Finished." +date diff --git a/hpc/batch_gpu/submit_breakdown_point_source_static_lattice_ab_a100_fp64 b/hpc/batch_gpu/submit_breakdown_point_source_static_lattice_ab_a100_fp64 new file mode 100755 index 000000000..eb6b87fae --- /dev/null +++ b/hpc/batch_gpu/submit_breakdown_point_source_static_lattice_ab_a100_fp64 @@ -0,0 +1,125 @@ +#!/bin/bash -l +# +# 2026-09 point-source CPU speed-up campaign, phase 3: the GPU regression leg +# for the static step-0 lattice (PyAutoArray#568). +# +# WHAT THIS MEASURES. The same interleaved, in-process four-route A/B as the RAL +# CPU submit (hpc/batch_cpu/submit_breakdown_point_source_static_lattice_ab_ral_cpu_fp64) +# on ONE RAL A100, constant folding disabled only (the production flags): +# +# scripts/point_source/likelihood_breakdown/static_lattice_ab.py +# control = flat 69 849-row step-0 table (phase-2 main path, in-job control); +# exact = 28 665 exact-float rows; lattice = 11 859 geometric vertices; +# library = the FEATURE BRANCH clones (LIBRARY below) -- must self-label +# library_matches = lattice. +# Rows: simple solved + plain + vmap-4 of solved; cluster solved + plain. +# Protocol: 20 rounds x 20 calls. The stop rule rejects phase 3 on any GPU +# regression, so the question here is lattice/library vs control >= 1. +# +# PRECISION. fp64 only (the PointSolver has no mixed-precision switch). +# +# LIBRARY. Prepends git clones of PyAutoArray and PyAutoLens +# feature/point-source-cpu-p3 to PYTHONPATH (prepare them as in the CPU submit's +# LIBRARY block). The job refuses to run unless autoarray and autolens import +# from those clones. +# +# BACKEND. JAX_PLATFORMS=cuda (NOT "cuda,cpu": a list can silently fall back to +# CPU) and JAX_PLATFORM_NAME=cuda; the job asserts jax.default_backend()=="gpu". +# XLA_FLAGS are byte-identical to the CPU submit's run 1. +# +# WALL-BASIS: +# Phase-2 A100 job 350587 ran the three-route vertex_dedup_ab cell (20 x 20, +# simple + cluster); this cell adds a fourth route and a step-0 gate row, so the +# laptop witness of THIS cell (975 s wall, folding off, 20 x 20, simple + cluster) is +# the basis, with 2.0 headroom for slower GPU compiles of the cluster HLO. +# cell: point_source/static_lattice_ab/simple device: gpu precision: fp64 +# source: measured-wall wall: 975 ref: laptop-point_source-static_lattice_ab_laptop_cpu_fp64 headroom: 2.0 + +#SBATCH -J static_lattice_ab_gpu +#SBATCH --partition=gpu +#SBATCH --gres=gpu:1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=8 +#SBATCH --mem=32gb +#SBATCH --time=3:00:00 +#SBATCH -o output/output.%A.out +#SBATCH -e error/error.%A.err +#SBATCH --mail-type=END,FAIL +#SBATCH --mail-user=james.w.nightingale@durham.ac.uk + +# Repo root from where this was submitted (hpc/batch_gpu/../..). sbatch from +# inside hpc/batch_gpu/. +export AP_ROOT="$(cd "${SLURM_SUBMIT_DIR:-$(dirname "$0")}/../.." && pwd)" +echo "AP_ROOT=$AP_ROOT" +source $AP_ROOT/activate.sh + +export AA_BRANCH_SRC="${AA_BRANCH_SRC:-/mnt/ral/jnightin/autolens_profiling_wt/PyAutoArray_point-source-cpu-p3}" +export AL_BRANCH_SRC="${AL_BRANCH_SRC:-/mnt/ral/jnightin/autolens_profiling_wt/PyAutoLens_point-source-cpu-p3}" +for src in "$AA_BRANCH_SRC/autoarray" "$AL_BRANCH_SRC/autolens"; do + if [ ! -f "$src/__init__.py" ]; then + echo "FATAL: $src is not a library checkout (see LIBRARY in the header)" >&2 + exit 2 + fi +done +export PYTHONPATH="$AA_BRANCH_SRC:$AL_BRANCH_SRC:$PYTHONPATH" + +export OMP_NUM_THREADS=1 +export MKL_NUM_THREADS=1 +export OPENBLAS_NUM_THREADS=1 +export NPROC=${SLURM_CPUS_PER_TASK:-8} +export JAX_PLATFORM_NAME=cuda +export JAX_PLATFORMS=cuda +export XLA_PYTHON_CLIENT_PREALLOCATE=false +export JAX_ENABLE_X64=1 +export XLA_FLAGS="--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false" +export NUMBA_CACHE_DIR=$AP_ROOT/output/numba_cache_${SLURM_JOB_ID:-local} +export MPLCONFIGDIR=/tmp/matplotlib_${SLURM_JOB_ID:-local} +mkdir -p "$NUMBA_CACHE_DIR" "$MPLCONFIGDIR" +# No persistent compile cache: compile_s is a gate and must be cold per route. +unset JAX_COMPILATION_CACHE_DIR + +echo "==========================================" +date +hostname +lscpu | grep 'Model name' || true +nvidia-smi --query-gpu=name,driver_version,memory.total,utilization.gpu --format=csv || true +echo "partition: ${SLURM_JOB_PARTITION:-unset}" +echo "gpus: ${CUDA_VISIBLE_DEVICES:-unset}" +echo "cpus-per-task: ${SLURM_CPUS_PER_TASK:-unset}" +echo "loadavg: $(cat /proc/loadavg)" +echo "Cell: point_source/likelihood_breakdown/static_lattice_ab" +echo "Backend: JAX_PLATFORMS=$JAX_PLATFORMS JAX_PLATFORM_NAME=$JAX_PLATFORM_NAME" +echo "XLA_FLAGS: $XLA_FLAGS" + +echo "=== source revisions ===" +for r in PyAutoNerves PyAutoFit PyAutoArray PyAutoGalaxy PyAutoLens; do + printf '%-14s %s\n' "$r (shared)" "$(git -C /mnt/ral/jnightin/PyAuto/$r rev-parse HEAD 2>&1)" +done +echo "PyAutoArray (branch clone, IMPORTED): $(git -C $AA_BRANCH_SRC rev-parse HEAD 2>&1)" +echo "PyAutoLens (branch clone, IMPORTED): $(git -C $AL_BRANCH_SRC rev-parse HEAD 2>&1)" +echo "AP_ROOT rev: $(git -C $AP_ROOT rev-parse HEAD 2>&1)" +echo "AP_ROOT branch: $(git -C $AP_ROOT rev-parse --abbrev-ref HEAD 2>&1)" + +cd $AP_ROOT + +echo "=== import provenance + backend assertion ===" +python3 -c " +import os, autoarray, autolens, autofit, autogalaxy, jax +for m in (autoarray, autolens, autofit, autogalaxy): + print(f'{m.__name__}.__file__ =', m.__file__) +print('jax', jax.__version__, jax.devices(), jax.default_backend()) +assert jax.default_backend() == 'gpu', 'JAX is not on the GPU' +assert os.path.realpath(autoarray.__file__).startswith(os.path.realpath(os.environ['AA_BRANCH_SRC'])), 'autoarray not imported from the branch clone' +assert os.path.realpath(autolens.__file__).startswith(os.path.realpath(os.environ['AL_BRANCH_SRC'])), 'autolens not imported from the branch clone' +print('ASSERT OK: GPU backend, autoarray + autolens from the branch clones') +" || exit 3 + +echo "==========================================" +echo "static-lattice A/B on A100 (20 x 20, simple + cluster)" +date +python3 -u scripts/point_source/likelihood_breakdown/static_lattice_ab.py --config-name hpc_ral_a100_fp64 --rounds 20 --calls 20 + +echo "==========================================" +nvidia-smi --query-gpu=name,memory.used --format=csv || true +echo "Finished." +date diff --git a/scripts/point_source/likelihood_breakdown/static_lattice_ab.py b/scripts/point_source/likelihood_breakdown/static_lattice_ab.py new file mode 100644 index 000000000..6b6704122 --- /dev/null +++ b/scripts/point_source/likelihood_breakdown/static_lattice_ab.py @@ -0,0 +1,1577 @@ +""" +JAX Profiling: PointSolver Static Step-0 Lattice A/B (point-source CPU campaign, phase 3) +======================================================================================== + +An interleaved, in-process A/B of the JAX ``PointSolver``'s step-0 triangle tiling +inside the production point-source likelihoods (PyAutoArray #568, phase 3). + +The mechanism +------------- + +``AbstractSolver.steps`` builds the step-0 tiling with +``AbstractSolver._initial_triangles`` and ray-traces ``triangles.vertices``. After +phase 2 (PyAutoArray ``681938ae``) the JAX ``vertices`` table is the flat ``(3N, 2)`` +per-triangle table: for the simple solver's +-9.9" / 0.2" lattice that is 69 849 +rows, of which only 28 665 are exact-float distinct and only **11 859** are +geometrically distinct (a lattice point shared by up to six triangles is computed +from each triangle's centre and the copies differ by ~1 ulp). The lattice depends +only on the solver geometry (static pytree aux data), so the unique table is a +compile-time constant: deflecting it and gathering back cuts the step-0 deflections +5.9x. After phase 2 deflections are 70 % of the simple call's FLOPs, and step 0 is +about half of them. + +The four routes +--------------- + +Each route swaps ``AbstractSolver._initial_triangles`` for the duration of +**tracing** only (:func:`route_injected`: signature check, call counters, restore by +identity): + +- ``control`` -- the flat ``(3N, 2)`` table: ``CoordinateArrayTriangles + .for_limits_and_scale`` with no table, i.e. the phase-2 main path (69 849 rows + for the simple lattice). +- ``exact`` -- a table of the exact-float distinct vertex rows (28 665) and the + inverse index map, built by this cell in NumPy. +- ``lattice`` -- the geometric table keyed on integer lattice positions (11 859), + built by this cell in NumPy with the same arithmetic as the library's + ``static_vertex_table``. +- ``library`` -- no substitution: whatever the imported PyAutoLens / PyAutoArray do. + It **self-labels** from the row count of the grid its step 0 deflects while + tracing: ``library_matches = control | exact | lattice | neither``. On the phase-3 + branch it must read ``lattice``; on main, ``control``. + +``exact`` and ``lattice`` are carried as a subclass of the library's +``CoordinateArrayTriangles`` whose ``_vertices_and_indices`` returns the cell's table, +so both routes run on PyAutoArray main as well as on the branch. Derived lattices +(``for_indexes`` / ``up_sample`` / ``neighborhood``) are built as the base class, so +steps >= 1 are identical in every route. + +Every route records the row count of its step-0 deflection grid (from a wrapper of +``AbstractSolver._plane_grid`` armed by the injected ``_initial_triangles``); a route +whose count is not its expected one, or whose substitute never ran while tracing, is a +FAIL, not a number. + +The measurement trap +-------------------- + +``jax`` caches traced jaxprs on function identity. Every route lowers a **fresh** +closure from a factory (fresh ``PointSolver`` and ``AnalysisPoint`` too) after +``jax.clear_caches()``. Once compiled an executable no longer depends on the patch, +so the four executables coexist and the timed calls run with nothing patched. + +The A/B protocol +---------------- + +Rows (``--models``): ``simple`` = the phase-1 harness model, dataset and solver (SIE, +``PointSolver.for_grid`` 100x100 @ 0.2", precision 0.001") with the fused +``FitPositionsImagePairAllSolved`` (``solved``) and ``FitPositionsImagePairAll`` +(``plain``) likelihoods, plus a ``vmap`` batch row of the solved likelihood; +``cluster`` = the two-source 13-component model (200x200 @ 0.7", precision 0.01") +with ``FitPositionsImagePairRepeat`` / ``...Solved`` summed over both systems. + +Each compiled likelihood takes the physical parameter vector and builds the instance +inside the trace. Per row: compile each route once, warm >= 3 calls, then +``--rounds`` (default 20) round-robin rounds, route order rotating, ``--calls`` +(default 20) calls per route per round. Per route: median / p10 / p90 / min / max +per-call ms; bootstrap 90 % intervals of the median ratios ``control / lattice``, +``control / exact``, ``exact / lattice`` and ``library / lattice``; the minimum +detectable improvement ``(p90 - p10) / median`` of the control route. + +Per route and row the cell also records: lower / compile / first-call seconds; +``cost_analysis`` FLOPs; ``compiled.memory_analysis()`` (argument / output / temp / +generated-code bytes); the process RSS before and after the lower + compile; the +HLO line count and hash; and, once per run, the cell table build time (cold and +cache hit), the table sizes in MB, the library ``static_vertex_table`` build and +cache-hit time when it exists, and the XLA CPU thread count observed after the +first compile (threads of this process), the scheduler affinity and ``os.cpu_count``. + +Correctness gates (the tolerance gate the human chose, 2026-09-24; recorded, never +asserted): log likelihood relative difference <= 1e-12 vs control on every streamed +instance; solved image positions: identical finite-image counts per instance and max +|delta| <= 1e-10; for the simple solved likelihood ``jax.grad`` finite, NON-zero and +``allclose(rtol=1e-10, atol=1e-12)``; ``vmap`` values equal to the per-instance scalar +values (rtol 1e-12); for simple plain, the step-0 ``containing_indices`` set of each +gate instance is identical across routes. + +Constant folding +---------------- + +Every PyAuto submit runs with ``--xla_disable_hlo_passes=constant_folding`` (set by +PyAutoNerves ``jax_wrapper`` whenever the substring is missing). With a constant +table, folding could pre-evaluate the control route's lattice arithmetic too, so the +comparison is re-run with folding ENABLED in a separate process: ``--constant-folding`` +replaces the flag, before ``jax`` is imported, with the dummy pass name +``--xla_disable_hlo_passes=constant_folding_probe_disabled`` -- it contains the +substring, so Nerves does not re-add the real flag, and names no real pass, so XLA +folds. The JSON's ``constant_folding.probe`` compiles a dot of two 1000-element +constants and records whether the optimised HLO entry still carries a computation +(folding off) or only a literal (folding on): the probe, not the flag string, is the evidence, and +the cell refuses to run when the probe disagrees with the request. (Elementwise +arithmetic on constants is folded in both modes, so it is no probe.) + +Output +------ + +``results/breakdown/point_source/static_lattice_ab_.{json,png}`` +(``..._constant_folding`` suffix for the folding run). As in the phase-2 cell the JSON +carries no top-level ``autolens_version``, so ``build_readme.py`` does not auto-table +it. A ``laptop_*`` config is load-inflated and is recorded ``quotable: false``. +""" + +import os +import sys +import time +from pathlib import Path + +# --constant-folding must act BEFORE jax (or autonerves) is imported. +CONSTANT_FOLDING = "--constant-folding" in sys.argv +_FOLDING_DISABLE = "--xla_disable_hlo_passes=constant_folding" +_FOLDING_DUMMY = "--xla_disable_hlo_passes=constant_folding_probe_disabled" +_XLA_FLAGS_REQUESTED = os.environ.get("XLA_FLAGS") +if CONSTANT_FOLDING: + _flags = [f for f in (_XLA_FLAGS_REQUESTED or "").split() if f != _FOLDING_DISABLE] + os.environ["XLA_FLAGS"] = " ".join(_flags + [_FOLDING_DUMMY]) + + +def _profiling_root() -> Path: + for parent in Path(__file__).resolve().parents: + if (parent / "ruff.toml").exists(): + return parent + raise RuntimeError("autolens_profiling root (ruff.toml) not found") + + +_ROOT = _profiling_root() +_MISC = str(_ROOT / "scripts" / "misc") +if _MISC not in sys.path: + sys.path.insert(0, _MISC) +if str(_ROOT) not in sys.path: + sys.path.insert(0, str(_ROOT)) + +import argparse +import contextlib +import functools +import hashlib +import inspect +import json +import re + +import jax +import jax.numpy as jnp +import jaxlib +import matplotlib +import numpy as np + +if os.environ.get("AUTOLENS_PROFILING_SMOKE") == "1": + print(f"[smoke] {__file__}: imports + module setup OK; exiting.") + sys.exit(0) + +from likelihood_breakdown.provenance import source_revisions, thread_environment # noqa: E402 +from likelihood_breakdown.timing import block # noqa: E402 + +from _profile_cli import ( # noqa: E402 + auto_simulate_if_missing, + device_info_dict, + machine_info_dict, + parse_profile_cli, + resolve_output_paths, +) + +_WALL_START = time.perf_counter() +_LOADAVG_START = os.getloadavg() + +_cli = parse_profile_cli() +_cell_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False) +_cell_parser.add_argument("--rounds", type=int, default=None) +_cell_parser.add_argument("--calls", type=int, default=None) +_cell_parser.add_argument("--vmap-batch", type=int, default=4) +_cell_parser.add_argument("--n-stream", type=int, default=16) +_cell_parser.add_argument("--warm", type=int, default=3) +_cell_parser.add_argument("--gate-instances", type=int, default=3) +_cell_parser.add_argument("--seed", type=int, default=568) +_cell_parser.add_argument("--models", default=None, help="comma list of simple,cluster") +_cell_parser.add_argument( + "--constant-folding", + action="store_true", + help="re-enable XLA constant folding (acted on before jax is imported; see docstring)", +) +_cell_parser.add_argument( + "--quick", action="store_true", help="rounds=5, calls=3, simple model only" +) +_args = _cli.parse_cell_args(_cell_parser) + +QUICK = bool(_args.quick) +N_ROUNDS = _args.rounds if _args.rounds is not None else (5 if QUICK else 20) +N_CALLS = _args.calls if _args.calls is not None else (3 if QUICK else 20) +N_STREAM = _args.n_stream +N_WARM = max(3, _args.warm) +N_GATE = max(3, _args.gate_instances) +VMAP_BATCH = _args.vmap_batch +SEED = _args.seed +MODELS = tuple( + m.strip() + for m in (_args.models or ("simple" if QUICK else "simple,cluster")).split(",") + if m.strip() +) +for _m in MODELS: + if _m not in ("simple", "cluster"): + raise SystemExit(f"--models: unknown model {_m!r} (expected simple / cluster)") +if N_ROUNDS < 1 or N_CALLS < 1 or VMAP_BATCH < 1: + raise SystemExit("--rounds, --calls and --vmap-batch must be positive") + +matplotlib.use("Agg") +import autoarray as aa # noqa: E402 +import autofit as af # noqa: E402 +import autolens as al # noqa: E402 +import matplotlib.pyplot as plt # noqa: E402 +from autoarray.structures.triangles import coordinate_array as _coordinate_array # noqa: E402 +from autoarray.structures.triangles.abstract import HEIGHT_FACTOR # noqa: E402 +from autoarray.structures.triangles.shape import Point # noqa: E402 +from autofit.jax import register_model as register_model_pytrees # noqa: E402 +from autolens.point.solver import shape_solver as _shape_solver # noqa: E402 + +ROUTES = ("control", "exact", "lattice", "library") +TARGET_DOTTED = "autolens.point.solver.shape_solver.AbstractSolver._initial_triangles" +BOOTSTRAP_SAMPLES = 2000 +BOOTSTRAP_SEED = 12345 +LOGL_RTOL = 1.0e-12 +POSITIONS_ATOL = 1.0e-10 +GRAD_RTOL = 1.0e-10 +GRAD_ATOL = 1.0e-12 +VMAP_RTOL = 1.0e-12 +RATIOS = ( + ("control", "lattice"), + ("control", "exact"), + ("exact", "lattice"), + ("library", "lattice"), + ("control", "library"), +) + + +# --------------------------------------------------------------------------- +# The cell's own step-0 tables (NumPy, never staged into a trace) +# --------------------------------------------------------------------------- + + +def _lattice_coordinates(y_min, y_max, x_min, x_max, scale) -> np.ndarray: + """The integer (y, x) lattice ``CoordinateArrayTriangles.for_limits_and_scale`` tiles.""" + y_shift = int(2 * y_min / scale) + x_shift = int(x_min / (HEIGHT_FACTOR * scale)) + coordinates = [] + for y in range(y_shift, int(2 * y_max / scale) + 1): + for x in range(x_shift - 1, int(x_max / (HEIGHT_FACTOR * scale)) + 2): + coordinates.append([y, x]) + return np.array(coordinates) + + +_TABLE_BUILD_S: dict = {} + + +@functools.lru_cache(maxsize=16) +def _cell_tables(y_min, y_max, x_min, x_max, scale) -> dict: + """``{"exact": (V, I), "lattice": (V, I), "coordinates": C}`` for one geometry.""" + t0 = time.perf_counter() + coordinates = _lattice_coordinates(y_min, y_max, x_min, x_max, scale) + flip = np.where((coordinates[:, 0] + coordinates[:, 1]) % 2 != 0, -1, 1)[:, None] + scaling = np.array([0.5 * scale, HEIGHT_FACTOR * scale]) + centres = scaling * coordinates + np.array([0.0, 0.0]) + triangles = np.stack( + ( + centres + flip * np.array([0.0, 0.5 * scale * HEIGHT_FACTOR]), + centres + flip * np.array([0.5 * scale, -0.5 * scale * HEIGHT_FACTOR]), + centres + flip * np.array([-0.5 * scale, -0.5 * scale * HEIGHT_FACTOR]), + ), + axis=1, + ) + flat = triangles.reshape(-1, 2) + + exact_vertices, exact_inverse = np.unique(flat, axis=0, return_inverse=True) + + offsets = np.array([[0, 1], [1, -1], [-1, -1]]) + keys = np.stack( + ( + coordinates[:, None, 0] + flip * offsets[None, :, 0], + 2 * coordinates[:, None, 1] + flip * offsets[None, :, 1], + ), + axis=-1, + ).reshape(-1, 2) + _, first, lattice_inverse = np.unique(keys, axis=0, return_index=True, return_inverse=True) + + tables = { + "coordinates": coordinates, + "exact": (exact_vertices, exact_inverse.reshape(-1, 3)), + "lattice": (flat[first].copy(), lattice_inverse.reshape(-1, 3)), + } + _TABLE_BUILD_S[(y_min, y_max, x_min, x_max, scale)] = time.perf_counter() - t0 + return tables + + +class _TableTriangles(_coordinate_array.CoordinateArrayTriangles): + """The library lattice with a cell-supplied ``(vertices, indices)`` table. + + Only ``_vertices_and_indices`` changes; every derived lattice is built as the base + class by the library's own methods, so steps >= 1 are untouched. + """ + + def __init__(self, coordinates, side_length, table): + super().__init__(coordinates=coordinates, side_length=side_length) + self._cell_table = table + + @property + def _vertices_and_indices(self): + vertices, indices = self._cell_table + return jnp.asarray(vertices), jnp.asarray(indices) + + +def _geometry(solver) -> tuple: + return ( + float(solver.y_min), + float(solver.y_max), + float(solver.x_min), + float(solver.x_max), + float(solver.scale), + ) + + +def _control_initial_triangles(self, xp): + """The phase-2 main path: the flat (3N, 2) per-triangle table, no table attached.""" + return _coordinate_array.CoordinateArrayTriangles.for_limits_and_scale( + y_min=self.y_min, y_max=self.y_max, x_min=self.x_min, x_max=self.x_max, scale=self.scale + ) + + +def _table_initial_triangles(kind): + def initial_triangles(self, xp): + tables = _cell_tables(*_geometry(self)) + return _TableTriangles( + coordinates=jnp.array(tables["coordinates"]), + side_length=self.scale, + table=tables[kind], + ) + + initial_triangles.__name__ = f"_{kind}_initial_triangles" + return initial_triangles + + +_exact_initial_triangles = _table_initial_triangles("exact") +_lattice_initial_triangles = _table_initial_triangles("lattice") + +_ROUTE_BODIES = { + "control": _control_initial_triangles, + "exact": _exact_initial_triangles, + "lattice": _lattice_initial_triangles, + "library": None, +} + + +def _body_provenance(fn) -> dict: + """Source text + bytecode hash of *fn*, captured at import (phase-2 provenance fix).""" + code = fn.__code__ + digest = hashlib.sha256(code.co_code + repr(code.co_consts).encode()).hexdigest()[:16] + source = inspect.getsource(fn) + return {"name": fn.__name__, "body": source, "bytecode_sha256": digest} + + +_BODY_PROVENANCE = { + "control": _body_provenance(_control_initial_triangles), + "table_factory": _body_provenance(_table_initial_triangles), + "tables": _body_provenance(_cell_tables.__wrapped__), + "table_triangles": { + "body": inspect.getsource(_TableTriangles), + "bytecode_sha256": hashlib.sha256( + _TableTriangles._vertices_and_indices.fget.__code__.co_code + ).hexdigest()[:16], + }, +} + + +@contextlib.contextmanager +def route_injected(route: str): + """Swap ``AbstractSolver._initial_triangles`` for *route* and record step-0 rows. + + Yields ``{"calls": n, "step0_rows": [...]}``: how often the route's tiling ran while + tracing and the row count of the grid each step 0 deflected. ``library`` wraps the + installed method only to count and record. Both patches are restored by identity. + """ + cls = _shape_solver.AbstractSolver + original = cls.__dict__["_initial_triangles"] + original_plane_grid = cls.__dict__["_plane_grid"] + params = list(inspect.signature(original).parameters) + if params != ["self", "xp"]: + raise TypeError( + f"{TARGET_DOTTED} now takes {params}; the substituted bodies take (self, xp). " + "The library changed under the injection -- update this harness, not the library." + ) + body = _ROUTE_BODIES[route] or original + record = {"calls": 0, "step0_rows": []} + armed = {"next": False} + + def counted(self, xp): + record["calls"] += 1 + armed["next"] = True + return body(self, xp) + + def plane_grid(self, tracer, grid, xp, plane_redshift=None): + if armed["next"]: + record["step0_rows"].append(int(np.shape(grid)[0])) + armed["next"] = False + return original_plane_grid(self, tracer, grid, xp, plane_redshift) + + cls._initial_triangles = counted + cls._plane_grid = plane_grid + try: + yield record + finally: + cls._initial_triangles = original + cls._plane_grid = original_plane_grid + if ( + cls.__dict__["_initial_triangles"] is not original + or cls.__dict__["_plane_grid"] is not original_plane_grid + ): + raise RuntimeError(f"{TARGET_DOTTED} was not restored by identity") + + +# --------------------------------------------------------------------------- +# Structural readouts +# --------------------------------------------------------------------------- + + +def _rss_bytes() -> int | None: + try: + with open("/proc/self/status") as f: + for line in f: + if line.startswith("VmRSS:"): + return int(line.split()[1]) * 1024 + except OSError: + return None + return None + + +def _thread_count() -> int | None: + try: + return len(os.listdir("/proc/self/task")) + except OSError: + return None + + +def _memory_analysis(compiled) -> dict | str: + try: + stats = compiled.memory_analysis() + except Exception as exc: # noqa: BLE001 -- optional on some backends + return f"unavailable: {type(exc).__name__}" + if stats is None: + return "unavailable: None" + out = {} + for name in ( + "argument_size_in_bytes", + "output_size_in_bytes", + "temp_size_in_bytes", + "alias_size_in_bytes", + "generated_code_size_in_bytes", + "host_temp_size_in_bytes", + ): + value = getattr(stats, name, None) + if value is not None: + out[name] = int(value) + return out + + +def _flops(compiled): + try: + cost = compiled.cost_analysis() + if isinstance(cost, list | tuple): + cost = cost[0] if cost else {} + value = (cost or {}).get("flops") + return float(value) if value is not None else None + except Exception as exc: # noqa: BLE001 -- cost analysis is optional on some backends + return f"unavailable: {type(exc).__name__}" + + +_HLO_CONSTANT = re.compile(r"=\s*(\w+)\[([\d,]*)\][^=]*\bconstant\(") + + +def _hlo_readout(compiled) -> dict: + hlo = compiled.as_text() + big = 0 + for m in _HLO_CONSTANT.finditer(hlo): + dims = [int(d) for d in m.group(2).split(",") if d] + if dims and int(np.prod(dims)) >= 1000: + big += 1 + return { + "hlo_sha256": hashlib.sha256(hlo.encode()).hexdigest()[:16], + "hlo_lines": hlo.count("\n"), + "hlo_large_constants": big, + } + + +_THREADS_AFTER_FIRST_COMPILE: dict = {} + + +def _compile_route(route: str, factory, example, *, label: str, expected_rows=None) -> dict: + """Lower a FRESH closure under *route* after ``jax.clear_caches()``; compile it.""" + jax.clear_caches() + fn = factory() + rss0 = _rss_bytes() + with route_injected(route) as record: + t0 = time.perf_counter() + lowered = jax.jit(fn).lower(example) + lower_s = time.perf_counter() - t0 + if route != "library" and record["calls"] == 0: + raise RuntimeError( + f"FAIL [{label}/{route}]: the substituted {TARGET_DOTTED} never ran while tracing." + ) + if expected_rows is not None and route != "library": + bad = [r for r in record["step0_rows"] if r != expected_rows[route]] + if bad or not record["step0_rows"]: + raise RuntimeError( + f"FAIL [{label}/{route}]: step-0 rows {record['step0_rows']} but the route's " + f"table has {expected_rows[route]} rows -- the injection did not take." + ) + t0 = time.perf_counter() + compiled = lowered.compile() + compile_s = time.perf_counter() - t0 + rss1 = _rss_bytes() + t0 = time.perf_counter() + first = block(compiled(example)) + first_call_s = time.perf_counter() - t0 + if not _THREADS_AFTER_FIRST_COMPILE: + _THREADS_AFTER_FIRST_COMPILE["threads"] = _thread_count() + _THREADS_AFTER_FIRST_COMPILE["label"] = f"{label}/{route}" + readout = _hlo_readout(compiled) + print( + f" [{label}/{route}] lower {lower_s:6.1f} s compile {compile_s:6.1f} s " + f"first {first_call_s * 1e3:8.2f} ms trace_calls {record['calls']:3d} " + f"step0_rows {sorted(set(record['step0_rows']))}" + ) + return { + "executable": compiled, + "first": first, + "record": { + "lower_s": float(lower_s), + "compile_s": float(compile_s), + "first_call_s": float(first_call_s), + "trace_calls": int(record["calls"]), + "step0_rows": record["step0_rows"], + "flops": _flops(compiled), + "memory_analysis": _memory_analysis(compiled), + "rss_before_lower_bytes": rss0, + "rss_after_compile_bytes": rss1, + "rss_delta_bytes": (rss1 - rss0) if rss0 is not None and rss1 is not None else None, + **readout, + }, + } + + +def _constant_folding_probe() -> dict: + """Does XLA fold constants in THIS process? Read from the optimised HLO, not the flags. + + The probe is a dot of two 1000-element constants. Elementwise arithmetic on + constants (``c * 2 + 1``, ``sin(c)``) comes back as a literal whether or not the + constant-folding pass is disabled, so it cannot tell the two modes apart; the + dot is live with ``--xla_disable_hlo_passes=constant_folding`` and folded to a + literal without it (measured on the laptop, jax 0.10.2 CPU, 2026-09-24). + """ + c = np.linspace(0.1, 1.0, 1000) + compiled = jax.jit(lambda: jnp.asarray(c) @ jnp.asarray(c)).lower().compile() + hlo = compiled.as_text() + entry = hlo[hlo.find("ENTRY") :] + # XLA may rewrite the dot (e.g. multiply + reduce-window), so "live" is any + # computation left in the entry; folded, the entry is only a constant. + live = bool(re.search(r"\b(fusion|dot|multiply|add|reduce|reduce-window)\(", entry)) + return { + "probe": "jit(lambda: c @ c), c = linspace(0.1, 1, 1000) constant", + "entry_has_computation": live, + "folding_ran": not live, + "entry_hlo": entry[:2000], + } + + +# --------------------------------------------------------------------------- +# Statistics +# --------------------------------------------------------------------------- + + +def _stats_ms(seconds) -> dict: + ms = np.asarray(seconds, dtype=float) * 1.0e3 + return { + "n": int(ms.size), + "median_ms": float(np.median(ms)), + "p10_ms": float(np.percentile(ms, 10)), + "p90_ms": float(np.percentile(ms, 90)), + "min_ms": float(ms.min()), + "max_ms": float(ms.max()), + "mean_ms": float(ms.mean()), + } + + +def _median_ratio(numerator, denominator, seed: int) -> dict: + num = np.asarray(numerator, dtype=float) + den = np.asarray(denominator, dtype=float) + rng = np.random.default_rng(seed) + boots = np.empty(BOOTSTRAP_SAMPLES) + for i in range(BOOTSTRAP_SAMPLES): + boots[i] = np.median(rng.choice(num, num.size)) / np.median(rng.choice(den, den.size)) + return { + "ratio": float(np.median(num) / np.median(den)), + "ci90_low": float(np.percentile(boots, 5)), + "ci90_high": float(np.percentile(boots, 95)), + "bootstrap_samples": BOOTSTRAP_SAMPLES, + } + + +def _array_delta(a, b) -> dict: + a = np.asarray(a, dtype=float) + b = np.asarray(b, dtype=float) + # Equal entries (including matching NaN / inf padding) contribute zero; an + # inf-vs-finite mismatch reports inf, never a NaN that nanmax would hide. + same = (a == b) | (np.isnan(a) & np.isnan(b)) + with np.errstate(invalid="ignore", over="ignore"): + diff = np.where(same, 0.0, np.abs(a - b)) + diff = np.where(np.isnan(diff), np.inf, diff) + scale = np.where(same, 1.0, np.maximum(np.abs(a), np.abs(b))) + rel = np.where(scale > 0, diff / np.where(scale > 0, scale, 1.0), diff) + return { + "bit_identical": bool(np.array_equal(a, b, equal_nan=True)), + "max_abs_delta": float(np.nanmax(diff)) if diff.size else 0.0, + "max_rel_delta": float(np.nanmax(rel)) if rel.size else 0.0, + "nan_pattern_equal": bool(np.array_equal(np.isnan(a), np.isnan(b))), + } + + +# --------------------------------------------------------------------------- +# Instance streams +# --------------------------------------------------------------------------- + + +def _vector_stream(model, n: int, seed: int) -> np.ndarray: + """Fixed-seed physical vectors: entry 0 the prior medians, the rest U(0.25,0.75) draws.""" + rng = np.random.default_rng(seed) + vectors = [np.asarray(model.physical_values_from_prior_medians, dtype=float)] + for _ in range(n - 1): + unit = rng.uniform(0.25, 0.75, size=model.prior_count) + vectors.append(np.asarray(model.vector_from_unit_vector(unit), dtype=float)) + return np.stack(vectors) + + +# --------------------------------------------------------------------------- +# The two models +# --------------------------------------------------------------------------- + + +def _simple_setup() -> dict: + """Phase-1 harness model, dataset and solver (image_plane.py).""" + instrument = "simple" + dataset_path = _ROOT / "dataset" / "point_source" / instrument + auto_simulate_if_missing( + dataset_path, dataset_type="point_source", instrument=instrument, workspace_root=_ROOT + ) + dataset = al.from_json(file_path=dataset_path / "point_dataset_positions_only.json") + + def mass_model(): + mass = af.Model(al.mp.Isothermal) + mass.centre.centre_0 = af.GaussianPrior(mean=0.0, sigma=0.005) + mass.centre.centre_1 = af.GaussianPrior(mean=0.0, sigma=0.005) + mass.einstein_radius = af.GaussianPrior(mean=1.6, sigma=0.05) + mass.ell_comps.ell_comps_0 = af.GaussianPrior(mean=0.05263158, sigma=0.01) + mass.ell_comps.ell_comps_1 = af.GaussianPrior(mean=0.0, sigma=0.01) + return mass + + def model(solved: bool): + lens = af.Model(al.Galaxy, redshift=0.5, mass=mass_model()) + if solved: + point = af.Model(al.ps.PointSolved) + else: + point = af.Model(al.ps.PointFlux) + point.centre.centre_0 = af.GaussianPrior(mean=0.07, sigma=0.005) + point.centre.centre_1 = af.GaussianPrior(mean=0.07, sigma=0.005) + source = af.Model(al.Galaxy, redshift=1.0, point_0=point) + return af.Collection(galaxies=af.Collection(lens=lens, source=source)) + + def solver(): + return al.PointSolver.for_grid( + grid=al.Grid2D.uniform(shape_native=(100, 100), pixel_scales=0.2), + pixel_scale_precision=0.001, + magnification_threshold=0.1, + ) + + fit_cls = { + "solved": al.FitPositionsImagePairAllSolved, + "plain": al.FitPositionsImagePairAll, + } + return { + "datasets": [dataset], + "models": {"solved": model(True), "plain": model(False)}, + "fit_cls": fit_cls, + "solver": solver, + "configuration": { + "dataset": "dataset/point_source/simple/point_dataset_positions_only.json", + "solver_grid_shape": [100, 100], + "solver_grid_pixel_scale": 0.2, + "solver_pixel_scale_precision": 0.001, + "magnification_threshold": 0.1, + "likelihoods": { + "solved": "FitPositionsImagePairAllSolved", + "plain": "FitPositionsImagePairAll", + }, + }, + } + + +def _cluster_setup() -> dict: + """The cluster cell's two-source 13-component model (image_plane.py step 5).""" + dataset_path = _ROOT / "dataset" / "cluster" / "simple" + auto_simulate_if_missing( + dataset_path, dataset_type="cluster", instrument="simple", workspace_root=_ROOT + ) + dataset_list = al.list_from_csv(file_path=dataset_path / "point_datasets.csv") + redshift_lens = 0.5 + main_lens_params = [((0.0, 0.0), 8.0, 20.0, 3.0), ((10.0, 8.0), 5.0, 12.0, 1.2)] + scaling_table = al.galaxy_table_from_csv(file_path=dataset_path / "scaling_galaxies.csv") + b0_ref, rs_ref, ra_scaling, exponent = 0.12, 10.0, 0.1, 0.5 + lum_ref = max(scaling_table.luminosities) + z_source_max = max(float(d.redshift) for d in dataset_list) + + # Step 1 of the cluster cell: back-trace the observed images through the + # fiducial tracer and take each system's centroid as its model source centre. + tracer = al.Tracer( + galaxies=[ + al.Galaxy( + redshift=redshift_lens, + mass=al.mp.dPIEMassB0Sph(centre=centre, ra=ra, rs=rs, b0=b0), + ) + for centre, ra, rs, b0 in main_lens_params + ] + + [ + al.Galaxy( + redshift=redshift_lens, + mass=al.mp.dPIEMassB0Sph( + centre=tuple(centre), + ra=ra_scaling, + rs=rs_ref * (lum / lum_ref) ** exponent, + b0=b0_ref * (lum / lum_ref) ** exponent, + ), + ) + for centre, lum in zip(scaling_table.centres, scaling_table.luminosities) + ] + + [ + al.Galaxy( + redshift=redshift_lens, + dark=al.mp.NFWMCRLudlowSph( + centre=(0.0, 0.0), + mass_at_200=10**15.3, + redshift_object=redshift_lens, + redshift_source=z_source_max, + ), + ) + ] + + [ + al.Galaxy(redshift=float(d.redshift), **{d.name: al.ps.Point(centre=(0.0, 0.0))}) + for d in dataset_list + ] + ) + source_centres = [] + for d in dataset_list: + plane_index = tracer.plane_index_via_redshift_from(redshift=float(d.redshift)) + traced = np.asarray( + tracer.traced_grid_2d_list_from( + grid=al.Grid2DIrregular(np.atleast_2d(np.asarray(d.positions))) + )[plane_index] + ) + source_centres.append(tuple(traced.mean(axis=0))) + + def gaussian(mean, fraction=0.01, floor=0.01): + return af.GaussianPrior(mean=float(mean), sigma=max(abs(float(mean)) * fraction, floor)) + + def model(solved: bool): + galaxies = {} + for i, (centre, ra, rs, b0) in enumerate(main_lens_params): + mass = af.Model(al.mp.dPIEMassB0Sph) + mass.centre.centre_0 = gaussian(centre[0]) + mass.centre.centre_1 = gaussian(centre[1]) + mass.ra = gaussian(ra) + mass.rs = gaussian(rs) + mass.b0 = gaussian(b0) + galaxies[f"main_{i}"] = af.Model(al.Galaxy, redshift=redshift_lens, mass=mass) + scaling_b0 = gaussian(b0_ref, floor=0.001) + for i, (centre, lum) in enumerate(zip(scaling_table.centres, scaling_table.luminosities)): + ratio = (lum / lum_ref) ** exponent + mass = af.Model(al.mp.dPIEMassB0Sph) + mass.centre = tuple(centre) + mass.ra = ra_scaling + mass.rs = rs_ref * ratio + mass.b0 = scaling_b0 * ratio + galaxies[f"scaling_{i}"] = af.Model(al.Galaxy, redshift=redshift_lens, mass=mass) + dark = af.Model( + al.mp.NFWMCRLudlowSph, + mass_at_200=10**15.3, + redshift_object=redshift_lens, + redshift_source=z_source_max, + ) + dark.centre.centre_0 = gaussian(0.0) + dark.centre.centre_1 = gaussian(0.0) + galaxies["host_halo"] = af.Model(al.Galaxy, redshift=redshift_lens, dark=dark) + for i, (d, centre) in enumerate(zip(dataset_list, source_centres)): + if solved: + point = af.Model(al.ps.PointSolved) + else: + point = af.Model(al.ps.Point) + point.centre.centre_0 = gaussian(centre[0]) + point.centre.centre_1 = gaussian(centre[1]) + galaxies[f"source_{i}"] = af.Model( + al.Galaxy, redshift=float(d.redshift), **{d.name: point} + ) + return af.Collection(galaxies=af.Collection(**galaxies)) + + def solver(): + return al.PointSolver.for_grid( + grid=al.Grid2D.uniform(shape_native=(200, 200), pixel_scales=0.7), + pixel_scale_precision=0.01, + use_jax=True, + ) + + fit_cls = { + "solved": al.FitPositionsImagePairRepeatSolved, + "plain": al.FitPositionsImagePairRepeat, + } + return { + "datasets": list(dataset_list), + "models": {"solved": model(True), "plain": model(False)}, + "fit_cls": fit_cls, + "solver": solver, + "configuration": { + "dataset": "dataset/cluster/simple/point_datasets.csv", + "n_systems": len(dataset_list), + "n_mass_profiles": len(main_lens_params) + len(scaling_table.luminosities) + 1, + "solver_grid_shape": [200, 200], + "solver_grid_pixel_scale": 0.7, + "solver_pixel_scale_precision": 0.01, + "source_centres_from": "back-traced observed-image centroids (cluster cell step 1)", + "likelihoods": { + "solved": "FitPositionsImagePairRepeatSolved (summed over systems)", + "plain": "FitPositionsImagePairRepeat (summed over systems)", + }, + }, + } + + +def _likelihood_factory(setup: dict, variant: str): + """A factory of FRESH ``vector -> log L`` closures (fresh solver + analyses per call).""" + model = setup["models"][variant] + + def factory(): + solver = setup["solver"]() + analyses = [ + al.AnalysisPoint( + dataset=d, solver=solver, fit_positions_cls=setup["fit_cls"][variant], use_jax=True + ) + for d in setup["datasets"] + ] + + def log_likelihood(vector): + instance = model.instance_from_vector(vector=vector, xp=jnp) + total = 0.0 + for analysis in analyses: + total = total + analysis.log_likelihood_function(instance=instance) + return total + + return log_likelihood + + return factory + + +def _positions_factory(setup: dict, variant: str): + """A factory of FRESH ``vector -> tuple(model positions per system)`` closures.""" + model = setup["models"][variant] + + def factory(): + solver = setup["solver"]() + analyses = [ + al.AnalysisPoint( + dataset=d, solver=solver, fit_positions_cls=setup["fit_cls"][variant], use_jax=True + ) + for d in setup["datasets"] + ] + + def positions(vector): + instance = model.instance_from_vector(vector=vector, xp=jnp) + out = [] + for analysis in analyses: + model_data = analysis.fit_from(instance=instance).positions.model_data + out.append(jnp.asarray(getattr(model_data, "array", model_data))) + return tuple(out) + + return positions + + return factory + + +def _step0_containing_factory(setup: dict): + """FRESH ``vector -> step-0 containing_indices`` closures (simple plain model).""" + model = setup["models"]["plain"] + + def factory(): + solver = setup["solver"]() + + def containing(vector): + instance = model.instance_from_vector(vector=vector, xp=jnp) + galaxies = list(instance.galaxies) + tracer = al.Tracer(galaxies=galaxies) + centre = instance.galaxies.source.point_0.centre + shape = Point(centre[0], centre[1]) + step = next(solver.steps(tracer=tracer, shape=shape, xp=jnp)) + return step.plane_triangles.containing_indices(shape=shape) + + return containing + + return factory + + +def _expected_rows(setup: dict) -> dict: + """Step-0 row count of each substituted route for this model's solver geometry.""" + solver = setup["solver"]() + geometry = _geometry(solver) + tables = _cell_tables(*geometry) + n = tables["coordinates"].shape[0] + library_lattice = _coordinate_array.CoordinateArrayTriangles.for_limits_and_scale( + y_min=solver.y_min, + y_max=solver.y_max, + x_min=solver.x_min, + x_max=solver.x_max, + scale=solver.scale, + ) + if not np.array_equal(np.asarray(library_lattice.coordinates), tables["coordinates"]): + raise RuntimeError("the cell's lattice coordinates differ from the library's") + return { + "geometry": list(geometry), + "n_triangles": int(n), + "rows": { + "control": 3 * int(n), + "exact": int(tables["exact"][0].shape[0]), + "lattice": int(tables["lattice"][0].shape[0]), + }, + } + + +def _table_provenance(setup: dict) -> dict: + """Table build time, cache-hit time and size, cell and (if present) library.""" + solver = setup["solver"]() + geometry = _geometry(solver) + tables = _cell_tables(*geometry) + t0 = time.perf_counter() + _cell_tables(*geometry) + hit_s = time.perf_counter() - t0 + out = { + "cell_build_ms": 1e3 * _TABLE_BUILD_S.get(geometry, float("nan")), + "cell_cache_hit_us": 1e6 * hit_s, + "exact_table_mb": (tables["exact"][0].nbytes + tables["exact"][1].nbytes) / 1e6, + "lattice_table_mb": (tables["lattice"][0].nbytes + tables["lattice"][1].nbytes) / 1e6, + } + builder = getattr(_coordinate_array, "static_vertex_table", None) + if builder is None: + out["library_static_vertex_table"] = "absent (library predates phase 3)" + return out + builder.cache_clear() + t0 = time.perf_counter() + vertices, indices = builder(*geometry) + cold = time.perf_counter() - t0 + t0 = time.perf_counter() + builder(*geometry) + hit = time.perf_counter() - t0 + lattice_vertices = tables["lattice"][0] + out["library_static_vertex_table"] = { + "build_ms": 1e3 * cold, + "cache_hit_us": 1e6 * hit, + "rows": int(vertices.shape[0]), + "mb": (vertices.nbytes + indices.nbytes) / 1e6, + "equals_cell_lattice_table": bool( + np.array_equal(vertices, lattice_vertices) + and np.array_equal(indices, tables["lattice"][1]) + ), + } + return out + + +# --------------------------------------------------------------------------- +# One interleaved timing row +# --------------------------------------------------------------------------- + + +def _timed_call(executable, argument): + t0 = time.perf_counter() + out = block(executable(argument)) + return time.perf_counter() - t0, out + + +def _run_row(label, factory, stream, *, expected_rows, batch=None) -> dict: + """Compile four routes, warm, interleave; return timings, values and structure.""" + print(f"\n--- row {label} ---") + if batch is None: + arguments = [jnp.asarray(v) for v in stream] + instance_ids = [[i] for i in range(len(stream))] + else: + n_batches = max(1, len(stream) // batch) + instance_ids = [ + [(b * batch + k) % len(stream) for k in range(batch)] for b in range(n_batches) + ] + arguments = [jnp.asarray(stream[ids]) for ids in instance_ids] + compiled = { + route: _compile_route( + route, factory, arguments[0], label=label, expected_rows=expected_rows + ) + for route in ROUTES + } + executables = {route: compiled[route]["executable"] for route in ROUTES} + + warm_s = {route: [] for route in ROUTES} + for route in ROUTES: + for w in range(N_WARM): + dt, _ = _timed_call(executables[route], arguments[w % len(arguments)]) + warm_s[route].append(dt) + + times = {route: [] for route in ROUTES} + per_round = {route: [] for route in ROUTES} + values = {route: {} for route in ROUTES} + deterministic = {route: True for route in ROUTES} + call_index = 0 + for r in range(N_ROUNDS): + order = ROUTES[r % len(ROUTES) :] + ROUTES[: r % len(ROUTES)] + start = call_index + for route in order: + round_times = [] + for c in range(N_CALLS): + k = (start + c) % len(arguments) + dt, out = _timed_call(executables[route], arguments[k]) + round_times.append(dt) + value = np.asarray(out, dtype=float) + if k in values[route]: + if not np.array_equal(values[route][k], value, equal_nan=True): + deterministic[route] = False + else: + values[route][k] = value + times[route].extend(round_times) + per_round[route].append(float(np.median(round_times))) + call_index = start + N_CALLS + + seen = sorted(set.intersection(*(set(values[r]) for r in ROUTES))) + value_arrays = {route: np.stack([values[route][k] for k in seen]) for route in ROUTES} + stats = {route: _stats_ms(times[route]) for route in ROUTES} + control = stats["control"] + ratios = { + f"{a}_over_{b}": _median_ratio(times[a], times[b], BOOTSTRAP_SEED + i) + for i, (a, b) in enumerate(RATIOS) + } + paired = np.asarray(per_round["control"]) / np.asarray(per_round["lattice"]) + row = { + "label": label, + "batch": batch, + "n_rounds": N_ROUNDS, + "n_calls_per_round": N_CALLS, + "n_warm": N_WARM, + "route_order": "round-robin, start rotated each round", + "per_call_ms": {route: [t * 1e3 for t in times[route]] for route in ROUTES}, + "warm_ms": {route: [t * 1e3 for t in warm_s[route]] for route in ROUTES}, + "stats": stats, + "ratios": ratios, + "paired_round_ratio_control_over_lattice": { + "median": float(np.median(paired)), + "p10": float(np.percentile(paired, 10)), + "p90": float(np.percentile(paired, 90)), + }, + "minimum_detectable_improvement": float( + (control["p90_ms"] - control["p10_ms"]) / control["median_ms"] + ), + "compile": {route: compiled[route]["record"] for route in ROUTES}, + "instances_compared": [instance_ids[k] for k in seen], + "values": {route: value_arrays[route].tolist() for route in ROUTES}, + "deterministic_within_route": deterministic, + "likelihood_gate": { + f"control_vs_{route}": _logl_gate(value_arrays["control"], value_arrays[route]) + for route in ROUTES[1:] + }, + } + if batch is None: + row["fiducial_log_likelihood"] = { + route: float(values[route][0]) for route in ROUTES if 0 in values[route] + } + r = ratios["control_over_lattice"] + print( + " median ms " + + " ".join(f"{route} {stats[route]['median_ms']:9.3f}" for route in ROUTES) + + f" control/lattice {r['ratio']:.3f} [{r['ci90_low']:.3f}, {r['ci90_high']:.3f}]" + ) + return row + + +def _logl_gate(a, b) -> dict: + delta = _array_delta(a, b) + delta["pass"] = bool(delta["nan_pattern_equal"] and delta["max_rel_delta"] <= LOGL_RTOL) + delta["rtol"] = LOGL_RTOL + return delta + + +def _positions_gate(label, factory, stream, *, expected_rows) -> dict: + print(f"\n--- positions gate {label} ---") + arguments = [jnp.asarray(v) for v in stream[:N_GATE]] + per_route = {} + compile_records = {} + for route in ROUTES: + compiled = _compile_route( + route, factory, arguments[0], label=f"{label}_positions", expected_rows=expected_rows + ) + compile_records[route] = compiled["record"] + per_route[route] = [ + [np.asarray(p, dtype=float) for p in block(compiled["executable"](a))] + for a in arguments + ] + + def finite_sorted(p): + p = p[np.all(np.isfinite(p), axis=-1)] + return p[np.lexsort((p[:, 1], p[:, 0]))] if p.size else p.reshape(0, 2) + + counts = { + route: [[int(np.isfinite(p).all(axis=-1).sum()) for p in inst] for inst in rows] + for route, rows in per_route.items() + } + pairs = {} + for route in ROUTES[1:]: + same_counts = counts[route] == counts["control"] + max_delta = 0.0 + if same_counts: + for inst_a, inst_b in zip(per_route["control"], per_route[route]): + for pa, pb in zip(inst_a, inst_b): + fa, fb = finite_sorted(pa), finite_sorted(pb) + if fa.size: + max_delta = max(max_delta, float(np.abs(fa - fb).max())) + flat_a = np.concatenate([np.ravel(p) for inst in per_route["control"] for p in inst]) + flat_b = np.concatenate([np.ravel(p) for inst in per_route[route] for p in inst]) + pairs[f"control_vs_{route}"] = { + "image_counts_identical": bool(same_counts), + "max_abs_delta_finite_sorted": max_delta if same_counts else None, + "padded_array_equal_nan_aware": bool(np.array_equal(flat_a, flat_b, equal_nan=True)), + "pass": bool(same_counts and max_delta <= POSITIONS_ATOL), + "atol": POSITIONS_ATOL, + } + return { + "n_instances": len(arguments), + "compile": compile_records, + "positions": { + route: [[p.tolist() for p in inst] for inst in per_route[route]] for route in ROUTES + }, + "finite_positions": counts, + "pairs": pairs, + } + + +def _grad_gate(label, factory, stream, *, expected_rows) -> dict: + print(f"\n--- gradient gate {label} ---") + arguments = [jnp.asarray(v) for v in stream[:N_GATE]] + + def grad_factory(): + return jax.grad(factory()) + + grads = {} + records = {} + for route in ROUTES: + compiled = _compile_route( + route, grad_factory, arguments[0], label=f"{label}_grad", expected_rows=expected_rows + ) + records[route] = compiled["record"] + grads[route] = np.stack( + [np.asarray(block(compiled["executable"](a)), dtype=float) for a in arguments] + ) + pairs = {} + for route in ROUTES[1:]: + delta = _array_delta(grads["control"], grads[route]) + delta["allclose"] = bool( + np.allclose(grads["control"], grads[route], rtol=GRAD_RTOL, atol=GRAD_ATOL) + ) + pairs[f"control_vs_{route}"] = delta + return { + "n_instances": len(arguments), + "rtol": GRAD_RTOL, + "atol": GRAD_ATOL, + "compile": records, + "all_finite": {route: bool(np.isfinite(g).all()) for route, g in grads.items()}, + "any_nonzero_per_instance": { + route: [bool(np.any(row != 0.0)) for row in g] for route, g in grads.items() + }, + "gradients": {route: g.tolist() for route, g in grads.items()}, + "pairs": pairs, + } + + +def _step0_gate(factory, stream, *, expected_rows) -> dict: + print("\n--- step-0 containing_indices gate simple_plain ---") + arguments = [jnp.asarray(v) for v in stream[:N_GATE]] + sets = {} + for route in ROUTES: + compiled = _compile_route( + route, factory, arguments[0], label="simple_plain_step0", expected_rows=expected_rows + ) + sets[route] = [] + for a in arguments: + idx = np.asarray(block(compiled["executable"](a))) + sets[route].append(sorted(int(i) for i in idx[idx >= 0])) + return { + "n_instances": len(arguments), + "sets": sets, + "pairs": { + f"control_vs_{route}": {"identical": sets[route] == sets["control"]} + for route in ROUTES[1:] + }, + } + + +def _vmap_gate(vmap_row: dict, scalar_row: dict) -> dict: + """``vmap`` batch values vs the scalar row's per-instance values, per route.""" + scalar_values = { + route: dict( + zip( + [ids[0] for ids in scalar_row["instances_compared"]], + np.asarray(scalar_row["values"][route]).ravel(), + ) + ) + for route in ROUTES + } + within = {} + for route in ROUTES: + batched, reference = [], [] + for ids, vals in zip(vmap_row["instances_compared"], vmap_row["values"][route]): + for i, v in zip(ids, np.ravel(vals)): + if i in scalar_values[route]: + batched.append(v) + reference.append(scalar_values[route][i]) + delta = _array_delta(batched, reference) + delta["allclose"] = bool( + np.allclose(batched, reference, rtol=VMAP_RTOL, atol=0.0, equal_nan=True) + ) + delta["n_compared"] = len(batched) + within[route] = delta + return {"vmap_vs_scalar_within_route": within, "across_routes": vmap_row["likelihood_gate"]} + + +def _library_label(rows: dict, expected: dict) -> dict: + per_row = {} + for label, row in rows.items(): + model_name = label.split("_")[0] + rows_expected = expected[model_name]["rows"] + library_rows = sorted(set(row["compile"]["library"]["step0_rows"])) + match = "neither" + if len(library_rows) == 1: + for route in ("control", "exact", "lattice"): + if library_rows[0] == rows_expected[route]: + match = route + hashes = {route: row["compile"][route]["hlo_sha256"] for route in ROUTES} + per_row[label] = { + "library_step0_rows": library_rows, + "expected_rows": rows_expected, + "library_matches": match, + "library_hlo_identical_to": [ + route for route in ROUTES[:3] if hashes[route] == hashes["library"] + ], + } + labels = {entry["library_matches"] for entry in per_row.values()} + overall = labels.pop() if len(labels) == 1 else "inconsistent" + return {"per_row": per_row, "library_matches": overall} + + +# =========================================================================== +# Run +# =========================================================================== + +constant_folding_probe = _constant_folding_probe() +print( + f"constant folding: requested={'ENABLED' if CONSTANT_FOLDING else 'disabled'} " + f"probe folding_ran={constant_folding_probe['folding_ran']} XLA_FLAGS={os.environ.get('XLA_FLAGS')}" +) +constant_folding_probe["agrees_with_request"] = ( + CONSTANT_FOLDING == constant_folding_probe["folding_ran"] +) +if not constant_folding_probe["agrees_with_request"]: + message = ( + f"constant folding requested={CONSTANT_FOLDING} but the HLO probe says " + f"folding_ran={constant_folding_probe['folding_ran']} (XLA_FLAGS={os.environ.get('XLA_FLAGS')})" + ) + # The probe was calibrated on the CPU backend; elsewhere record, do not refuse. + if jax.default_backend() == "cpu": + raise RuntimeError(f"FAIL: {message}") + print(f"WARNING: {message} -- probe calibrated on CPU only; recorded in the JSON") + +rows: dict[str, dict] = {} +gates: dict[str, dict] = {} +model_configurations: dict[str, dict] = {} +free_parameters: dict[str, int] = {} +expected: dict[str, dict] = {} +tables_provenance: dict[str, dict] = {} + +for model_name in MODELS: + setup = _simple_setup() if model_name == "simple" else _cluster_setup() + model_configurations[model_name] = setup["configuration"] + expected[model_name] = _expected_rows(setup) + tables_provenance[model_name] = _table_provenance(setup) + expected_rows = expected[model_name]["rows"] + print( + f"\n=== {model_name}: step-0 rows {expected_rows}; tables {tables_provenance[model_name]}" + ) + # Load-bearing for the gradient gate: without it jax.grad of this likelihood + # comes back identically ZERO (forward values unaffected). + for _model in setup["models"].values(): + register_model_pytrees(_model) + for variant in ("solved", "plain"): + model = setup["models"][variant] + free_parameters[f"{model_name}_{variant}"] = int(model.prior_count) + stream = _vector_stream(model, N_STREAM, SEED + (0 if variant == "solved" else 1)) + factory = _likelihood_factory(setup, variant) + label = f"{model_name}_{variant}" + rows[label] = _run_row(label, factory, stream, expected_rows=expected_rows) + if variant == "solved": + gates[f"{label}_positions"] = _positions_gate( + label, _positions_factory(setup, variant), stream, expected_rows=expected_rows + ) + if label == "simple_solved": + gates[f"{label}_grad"] = _grad_gate(label, factory, stream, expected_rows=expected_rows) + vlabel = f"{label}_vmap{VMAP_BATCH}" + rows[vlabel] = _run_row( + vlabel, + lambda f=factory: jax.vmap(f()), + stream, + expected_rows=expected_rows, + batch=VMAP_BATCH, + ) + gates[vlabel] = _vmap_gate(rows[vlabel], rows[label]) + if label == "simple_plain": + gates["simple_plain_step0_containing"] = _step0_gate( + _step0_containing_factory(setup), stream, expected_rows=expected_rows + ) + +library_label = _library_label(rows, expected) + + +def _gate_summary() -> dict: + """One flat verdict table: every gate, control vs each other route.""" + summary = {} + for label, row in rows.items(): + for pair, g in row["likelihood_gate"].items(): + summary[f"{label}.log_likelihood.{pair}"] = { + "pass": g["pass"], + "bit_identical": g["bit_identical"], + "max_rel_delta": g["max_rel_delta"], + } + for label, gate in gates.items(): + if label.endswith("_positions"): + for pair, g in gate["pairs"].items(): + summary[f"{label}.{pair}"] = { + "pass": g["pass"], + "image_counts_identical": g["image_counts_identical"], + "max_abs_delta": g["max_abs_delta_finite_sorted"], + } + elif label.endswith("_grad"): + nonzero = all(all(v) for v in gate["any_nonzero_per_instance"].values()) + for pair, g in gate["pairs"].items(): + summary[f"{label}.{pair}"] = { + "pass": g["allclose"] and all(gate["all_finite"].values()) and nonzero, + "non_zero": nonzero, + "bit_identical": g["bit_identical"], + "max_rel_delta": g["max_rel_delta"], + } + elif label.endswith("_step0_containing"): + for pair, g in gate["pairs"].items(): + summary[f"{label}.{pair}"] = {"pass": g["identical"]} + else: + for route, g in gate["vmap_vs_scalar_within_route"].items(): + summary[f"{label}.vmap_vs_scalar.{route}"] = { + "pass": g["allclose"], + "bit_identical": g["bit_identical"], + "max_abs_delta": g["max_abs_delta"], + } + return summary + + +gate_summary = _gate_summary() +all_gates_pass = all(v["pass"] for v in gate_summary.values()) + +config_name = _cli.config_name or ( + "local_cpu_fp64" if jax.default_backend() == "cpu" else "unlabelled_device_fp64" +) +_home = str(Path.home()) + + +def _scrub(obj): + """Replace the home directory in every string (no /home/ in a committed JSON).""" + if isinstance(obj, dict): + return {k: _scrub(v) for k, v in obj.items()} + if isinstance(obj, list | tuple): + return [_scrub(v) for v in obj] + if isinstance(obj, str) and _home and _home != "/": + return obj.replace(_home, "~") + return obj + + +device = device_info_dict() +if device.get("jax_compilation_cache_dir"): + device["jax_compilation_cache_dir"] = ( + "/" + Path(device["jax_compilation_cache_dir"]).name + ) + +summary = { + "cell": "static_lattice_ab", + "issue": "PyAutoLabs/PyAutoArray#568", + "phase": 3, + "config_name": config_name, + "quotable": not config_name.startswith(("laptop", "local")), + "host_note": ( + "laptop witness: WSL2, interactive load, NOT quotable -- ratios indicative only" + if config_name.startswith(("laptop", "local")) + else None + ), + "quick": QUICK, + "models": list(MODELS), + "constant_folding": { + "requested": CONSTANT_FOLDING, + "xla_flags_requested": _XLA_FLAGS_REQUESTED, + "xla_flags_effective": os.environ.get("XLA_FLAGS"), + "probe": constant_folding_probe, + }, + "library_versions": { + "autolens": al.__version__, + "autoarray": aa.__version__, + "autofit": af.__version__, + }, + "package_versions": {"jax": jax.__version__, "jaxlib": jaxlib.__version__}, + "source_revisions": source_revisions(_ROOT), + "autoarray_imported_from": str(Path(aa.__file__).resolve().parent.parent), + "autolens_imported_from": str(Path(al.__file__).resolve().parent.parent), + "target": TARGET_DOTTED, + "route_bodies": _BODY_PROVENANCE, + "body_provenance_captured": "import time", + "expected_step0_rows": expected, + "tables": tables_provenance, + "device": device, + "jax_devices": [str(d) for d in jax.devices()], + "machine": machine_info_dict(), + "thread_environment": thread_environment(), + "xla_threads_observed_after_first_compile": _THREADS_AFTER_FIRST_COMPILE, + "threads_at_end": _thread_count(), + "sched_affinity": len(os.sched_getaffinity(0)), + "os_cpu_count": os.cpu_count(), + "xla_flags": os.environ.get("XLA_FLAGS"), + "jax_platforms": os.environ.get("JAX_PLATFORMS"), + "jax_enable_x64": bool(jax.config.jax_enable_x64), + "loadavg_start": list(_LOADAVG_START), + "loadavg_end": list(os.getloadavg()), + "protocol": { + "routes": list(ROUTES), + "n_rounds": N_ROUNDS, + "n_calls_per_round": N_CALLS, + "n_warm": N_WARM, + "n_stream": N_STREAM, + "n_gate_instances": N_GATE, + "vmap_batch": VMAP_BATCH, + "instance_seed": SEED, + "instance_stream": "entry 0 = prior medians; rest = vector_from_unit_vector(U(0.25,0.75))", + "argument": "physical parameter vector; instance_from_vector(xp=jnp) inside the trace", + "cache_trap_guard": "fresh closure + fresh solver/analyses per route; jax.clear_caches()", + "bootstrap": {"samples": BOOTSTRAP_SAMPLES, "seed": BOOTSTRAP_SEED, "interval": "90%"}, + "gates": { + "log_likelihood_rtol": LOGL_RTOL, + "positions_atol": POSITIONS_ATOL, + "grad_rtol": GRAD_RTOL, + "grad_atol": GRAD_ATOL, + "vmap_rtol": VMAP_RTOL, + "cluster": "log-likelihood and positions only (no cluster grad/vmap/step-0 sets)", + }, + "pytrees": "autofit.jax.register_model(model) for every model before any trace", + }, + "free_parameters": free_parameters, + "model_configuration": model_configurations, + "library_label": library_label, + "library_matches": library_label["library_matches"], + "all_gates_pass": all_gates_pass, + "gate_summary": gate_summary, + "rows": rows, + "gates": gates, + "wall_s": float(time.perf_counter() - _WALL_START), +} + +dict_path, chart_path = resolve_output_paths( + _cli, + default_dir=_ROOT / "results" / "breakdown" / "point_source", + default_basename="static_lattice_ab_local", + cell="static_lattice_ab" + ("_constant_folding" if CONSTANT_FOLDING else ""), +) +dict_path.write_text(json.dumps(_scrub(summary), indent=2)) + +# --------------------------------------------------------------------------- +# PNG: per-row boxplots, the four routes +# --------------------------------------------------------------------------- +colours = {"control": "#C44E52", "exact": "#DD8452", "lattice": "#4C72B0", "library": "#8C8C8C"} +fig, axes = plt.subplots( + nrows=1, ncols=len(rows), figsize=(4.4 * len(rows), 5.0), constrained_layout=True +) +axes = np.atleast_1d(axes) +for ax, (label, row) in zip(axes, rows.items()): + data = [row["per_call_ms"][route] for route in ROUTES] + box = ax.boxplot(data, whis=(10, 90), showfliers=True, patch_artist=True) + for patch, route in zip(box["boxes"], ROUTES): + patch.set_facecolor(colours[route]) + patch.set_alpha(0.6) + ax.set_xticks(range(1, len(ROUTES) + 1)) + ax.set_xticklabels(ROUTES) + ax.set_ylabel("ms per call" + (f" (batch of {row['batch']})" if row["batch"] else "")) + r = row["ratios"]["control_over_lattice"] + e = row["ratios"]["control_over_exact"] + ax.set_title( + f"{label}\ncontrol/lattice {r['ratio']:.2f}x [{r['ci90_low']:.2f}, {r['ci90_high']:.2f}]" + f"\ncontrol/exact {e['ratio']:.2f}x", + fontsize=9, + ) + ax.set_ylim(bottom=0) +fig.suptitle( + f"PointSolver static step-0 lattice A/B ({config_name}" + f"{', constant folding ON' if CONSTANT_FOLDING else ''}); " + f"library_matches={library_label['library_matches']}; all_gates_pass={all_gates_pass}; " + "whiskers p10/p90", + fontsize=10, +) +fig.savefig(chart_path, dpi=150) +plt.close(fig) + +print("\n" + "=" * 110) +print( + f"STATIC-LATTICE A/B ({config_name}, rounds={N_ROUNDS}, calls/round={N_CALLS}, " + f"constant_folding={'ON' if CONSTANT_FOLDING else 'off'})" +) +print("=" * 110) +print( + f" {'row':<22} {'route':<8} {'median':>9} {'p10':>9} {'p90':>9} {'min':>9}" + f" {'step0':>6} {'MFLOP':>8} {'temp_MB':>8} {'compile_s':>9}" +) +for label, row in rows.items(): + for route in ROUTES: + s = row["stats"][route] + c = row["compile"][route] + flops = c["flops"] / 1e6 if isinstance(c["flops"], float) else float("nan") + mem = c["memory_analysis"] + temp = ( + mem.get("temp_size_in_bytes", float("nan")) / 1e6 + if isinstance(mem, dict) + else float("nan") + ) + step0 = c["step0_rows"][0] if c["step0_rows"] else -1 + print( + f" {label:<22} {route:<8} {s['median_ms']:9.3f} {s['p10_ms']:9.3f} {s['p90_ms']:9.3f}" + f" {s['min_ms']:9.3f} {step0:6d} {flops:8.2f} {temp:8.2f} {c['compile_s']:9.1f}" + ) + for name, r in row["ratios"].items(): + print( + f" {'':<22} {name:<22} {r['ratio']:.3f} [90% {r['ci90_low']:.3f}, {r['ci90_high']:.3f}]" + ) + print(f" {'':<22} MDI {row['minimum_detectable_improvement']:.3f}") +print("-" * 110) +print(f" library_matches: {library_label['library_matches']}") +print(f" all_gates_pass: {all_gates_pass}") +for name, verdict in gate_summary.items(): + if not verdict["pass"]: + print(f" GATE NOT PASSED: {name}: {verdict}") +print(f" wall: {summary['wall_s']:.0f} s") +print(f" Results JSON: {dict_path}") +print(f" Results PNG: {chart_path}") From 3da2583d46b80f72793caf11a16433ca05f00d20 Mon Sep 17 00:00:00 2001 From: Jammy2211 Date: Thu, 24 Sep 2026 14:19:54 +0200 Subject: [PATCH 2/6] profiling(point_source): pin the phase-3 RAL CPU submit to euclid-ral-compute-10-2 (compute-1..19 not responding) Co-Authored-By: Claude Opus 5.5 --- ...wn_point_source_static_lattice_ab_ral_cpu_fp64 | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/hpc/batch_cpu/submit_breakdown_point_source_static_lattice_ab_ral_cpu_fp64 b/hpc/batch_cpu/submit_breakdown_point_source_static_lattice_ab_ral_cpu_fp64 index cab0fc2e5..7e4288eb4 100755 --- a/hpc/batch_cpu/submit_breakdown_point_source_static_lattice_ab_ral_cpu_fp64 +++ b/hpc/batch_cpu/submit_breakdown_point_source_static_lattice_ab_ral_cpu_fp64 @@ -31,11 +31,14 @@ # # HOST PINNING. Phase 1 landed on euclid-ral-compute-10-2 (Xeon 8490H) and # phase 2 on euclid-ral-compute-1 (EPYC 7763); cluster control differed 15-20 % -# between them (host, not drift). This job is pinned to the phase-2 host with -# --nodelist so phase-2 and phase-3 numbers share a CPU; the control route is -# measured in the same job regardless, and only in-job ratios are quoted. -# If euclid-ral-compute-1 is down or saturated (sinfo -p ral -N -o "%N %t %C"), -# edit --nodelist to another node and say so in the campaign note. +# between them (host, not drift). The phase-2 host was the intended pin, but on +# 2026-09-24 euclid-ral-compute-1..19 were all IDLE+NOT_RESPONDING in sinfo and +# only the euclid-ral-compute-10-* (Xeon 8490H) nodes answered, so this job is +# pinned with --nodelist to euclid-ral-compute-10-2 -- the PHASE-1 host. The +# control route is measured in the same job regardless, and only in-job ratios +# are quoted; phase-2 EPYC milliseconds are not comparable to these. +# Check before submitting: sinfo -p ral -N -o "%N %t %C" (a trailing * = not +# responding). If 10-2 is busy, pin another 10-* node and say so in the note. # # LIBRARY. The shared /mnt/ral/jnightin/PyAuto mirror is main. This submit # PREPENDS git clones of PyAutoArray and PyAutoLens feature/point-source-cpu-p3 @@ -70,7 +73,7 @@ #SBATCH -J static_lattice_ab #SBATCH --partition=ral -#SBATCH --nodelist=euclid-ral-compute-1 +#SBATCH --nodelist=euclid-ral-compute-10-2 #SBATCH --ntasks=1 #SBATCH --cpus-per-task=8 #SBATCH --mem=32gb From 93aac9bb354148160f6eac0c1850467b262c224a Mon Sep 17 00:00:00 2001 From: Jammy2211 Date: Thu, 24 Sep 2026 14:49:21 +0200 Subject: [PATCH 3/6] results(point_source): phase-3 static-lattice laptop witness (folding off/on) + campaign note section + README line Laptop, load-inflated, not quotable: simple 1.9x, cluster 2.7x (folding on 2.0x / 2.1x), library_matches=lattice, 31/31 gates bit-identical. Compile effect unresolved on the laptop; RAL CPU (pinned) and A100 legs pending. Co-Authored-By: Claude Opus 5.5 --- README.md | 6 + ...e_ab_constant_folding_laptop_cpu_fp64.json | 13576 ++++++++++++++++ ...ce_ab_constant_folding_laptop_cpu_fp64.png | Bin 0 -> 167322 bytes .../static_lattice_ab_laptop_cpu_fp64.json | 13576 ++++++++++++++++ .../static_lattice_ab_laptop_cpu_fp64.png | Bin 0 -> 173681 bytes results/notes/point_source_cpu_campaign.md | 98 +- 6 files changed, 27252 insertions(+), 4 deletions(-) create mode 100644 results/breakdown/point_source/static_lattice_ab_constant_folding_laptop_cpu_fp64.json create mode 100644 results/breakdown/point_source/static_lattice_ab_constant_folding_laptop_cpu_fp64.png create mode 100644 results/breakdown/point_source/static_lattice_ab_laptop_cpu_fp64.json create mode 100644 results/breakdown/point_source/static_lattice_ab_laptop_cpu_fp64.png diff --git a/README.md b/README.md index 259e21b22..1ac8b817d 100644 --- a/README.md +++ b/README.md @@ -264,6 +264,12 @@ separately per likelihood × transform. Standing conclusions: (simple solved 1.70 → 0.84 ms, cluster 4.21 → 2.46 ms), every gate bit-identical. Accepted; the post-fix call is deflection-dominated (70 % of FLOPs), so phase 3 is the static initial-lattice precompute. Findings: [`results/notes/point_source_cpu_campaign.md`](./results/notes/point_source_cpu_campaign.md). +- **Point-source CPU speed-up, phase 3 (2026-09, in progress)** — precompute the JAX PointSolver's + static step-0 lattice: deflect the 11 859 geometrically unique vertices instead of the 69 849 flat + slots (PyAutoArray #568). Four-route A/B cell `static_lattice_ab.py`. Laptop witness only + (load-inflated, not quotable): simple 1.9×, cluster 2.7×, FLOPs −52 % / −72 %, every gate + bit-identical; compile-time effect unresolved on the laptop. RAL CPU (pinned) and A100 legs pending. + Findings: [`results/notes/point_source_cpu_campaign.md`](./results/notes/point_source_cpu_campaign.md). ## How to read this repo diff --git a/results/breakdown/point_source/static_lattice_ab_constant_folding_laptop_cpu_fp64.json b/results/breakdown/point_source/static_lattice_ab_constant_folding_laptop_cpu_fp64.json new file mode 100644 index 000000000..3004c4701 --- /dev/null +++ b/results/breakdown/point_source/static_lattice_ab_constant_folding_laptop_cpu_fp64.json @@ -0,0 +1,13576 @@ +{ + "cell": "static_lattice_ab", + "issue": "PyAutoLabs/PyAutoArray#568", + "phase": 3, + "config_name": "laptop_cpu_fp64", + "quotable": false, + "host_note": "laptop witness: WSL2, interactive load, NOT quotable -- ratios indicative only", + "quick": false, + "models": [ + "simple", + "cluster" + ], + "constant_folding": { + "requested": true, + "xla_flags_requested": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false", + "xla_flags_effective": "--xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false --xla_disable_hlo_passes=constant_folding_probe_disabled", + "probe": { + "probe": "jit(lambda: c @ c), c = linspace(0.1, 1, 1000) constant", + "entry_has_computation": false, + "folding_ran": true, + "entry_hlo": "ENTRY %main.1 () -> f64[] {\n %constant.4 = f64[] constant(370.13513513513516), metadata={op_name=\"jit()/dot_general\" stack_frame_id=3}\n ROOT %copy = f64[] copy(%constant.4)\n}\n\n", + "agrees_with_request": true + } + }, + "library_versions": { + "autolens": "2026.8.17.1", + "autoarray": "2026.8.17.1", + "autofit": "2026.8.17.1" + }, + "package_versions": { + "jax": "0.10.2", + "jaxlib": "0.10.2" + }, + "source_revisions": { + "autolens_profiling": "3da2583d46b80f72793caf11a16433ca05f00d20", + "PyAutoNerves": "1fa613aa8d89a79613009fb66f5b4e2d21deb57a", + "PyAutoFit": "a736840127be7a3c21e15a5c945e94d8f1082983", + "PyAutoArray": "ad0bf97b45d572effa5d428a101d32ac4893b950", + "PyAutoGalaxy": "70a61e26cd715cefe7012a38285698091246a8a4", + "PyAutoLens": "b346b6a0a092098f23964e274edb220ea9757ad0" + }, + "autoarray_imported_from": "~/Code/PyAutoLabs-wt/point-source-cpu-p3/PyAutoArray", + "autolens_imported_from": "~/Code/PyAutoLabs-wt/point-source-cpu-p3/PyAutoLens", + "target": "autolens.point.solver.shape_solver.AbstractSolver._initial_triangles", + "route_bodies": { + "control": { + "name": "_control_initial_triangles", + "body": "def _control_initial_triangles(self, xp):\n \"\"\"The phase-2 main path: the flat (3N, 2) per-triangle table, no table attached.\"\"\"\n return _coordinate_array.CoordinateArrayTriangles.for_limits_and_scale(\n y_min=self.y_min, y_max=self.y_max, x_min=self.x_min, x_max=self.x_max, scale=self.scale\n )\n", + "bytecode_sha256": "decd881cb4e376c8" + }, + "table_factory": { + "name": "_table_initial_triangles", + "body": "def _table_initial_triangles(kind):\n def initial_triangles(self, xp):\n tables = _cell_tables(*_geometry(self))\n return _TableTriangles(\n coordinates=jnp.array(tables[\"coordinates\"]),\n side_length=self.scale,\n table=tables[kind],\n )\n\n initial_triangles.__name__ = f\"_{kind}_initial_triangles\"\n return initial_triangles\n", + "bytecode_sha256": "244334b9dfb14504" + }, + "tables": { + "name": "_cell_tables", + "body": "@functools.lru_cache(maxsize=16)\ndef _cell_tables(y_min, y_max, x_min, x_max, scale) -> dict:\n \"\"\"``{\"exact\": (V, I), \"lattice\": (V, I), \"coordinates\": C}`` for one geometry.\"\"\"\n t0 = time.perf_counter()\n coordinates = _lattice_coordinates(y_min, y_max, x_min, x_max, scale)\n flip = np.where((coordinates[:, 0] + coordinates[:, 1]) % 2 != 0, -1, 1)[:, None]\n scaling = np.array([0.5 * scale, HEIGHT_FACTOR * scale])\n centres = scaling * coordinates + np.array([0.0, 0.0])\n triangles = np.stack(\n (\n centres + flip * np.array([0.0, 0.5 * scale * HEIGHT_FACTOR]),\n centres + flip * np.array([0.5 * scale, -0.5 * scale * HEIGHT_FACTOR]),\n centres + flip * np.array([-0.5 * scale, -0.5 * scale * HEIGHT_FACTOR]),\n ),\n axis=1,\n )\n flat = triangles.reshape(-1, 2)\n\n exact_vertices, exact_inverse = np.unique(flat, axis=0, return_inverse=True)\n\n offsets = np.array([[0, 1], [1, -1], [-1, -1]])\n keys = np.stack(\n (\n coordinates[:, None, 0] + flip * offsets[None, :, 0],\n 2 * coordinates[:, None, 1] + flip * offsets[None, :, 1],\n ),\n axis=-1,\n ).reshape(-1, 2)\n _, first, lattice_inverse = np.unique(keys, axis=0, return_index=True, return_inverse=True)\n\n tables = {\n \"coordinates\": coordinates,\n \"exact\": (exact_vertices, exact_inverse.reshape(-1, 3)),\n \"lattice\": (flat[first].copy(), lattice_inverse.reshape(-1, 3)),\n }\n _TABLE_BUILD_S[(y_min, y_max, x_min, x_max, scale)] = time.perf_counter() - t0\n return tables\n", + "bytecode_sha256": "3b7bdf752e1994f7" + }, + "table_triangles": { + "body": "class _TableTriangles(_coordinate_array.CoordinateArrayTriangles):\n \"\"\"The library lattice with a cell-supplied ``(vertices, indices)`` table.\n\n Only ``_vertices_and_indices`` changes; every derived lattice is built as the base\n class by the library's own methods, so steps >= 1 are untouched.\n \"\"\"\n\n def __init__(self, coordinates, side_length, table):\n super().__init__(coordinates=coordinates, side_length=side_length)\n self._cell_table = table\n\n @property\n def _vertices_and_indices(self):\n vertices, indices = self._cell_table\n return jnp.asarray(vertices), jnp.asarray(indices)\n", + "bytecode_sha256": "6308d3338aa3791a" + } + }, + "body_provenance_captured": "import time", + "expected_step0_rows": { + "simple": { + "geometry": [ + -9.9, + 9.9, + -9.9, + 9.9, + 0.2 + ], + "n_triangles": 23283, + "rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + } + }, + "cluster": { + "geometry": [ + -69.64999999999999, + 69.64999999999999, + -69.64999999999999, + 69.64999999999999, + 0.7 + ], + "n_triangles": 92169, + "rows": { + "control": 276507, + "exact": 110085, + "lattice": 46516 + } + } + }, + "tables": { + "simple": { + "cell_build_ms": 147.54220000031637, + "cell_cache_hit_us": 0.8999995770864189, + "exact_table_mb": 1.017432, + "lattice_table_mb": 0.748536, + "library_static_vertex_table": { + "build_ms": 170.2930000028573, + "cache_hit_us": 2.7999994927085936, + "rows": 11859, + "mb": 0.748536, + "equals_cell_lattice_table": true + } + }, + "cluster": { + "cell_build_ms": 550.0634999989416, + "cell_cache_hit_us": 0.700001692166552, + "exact_table_mb": 3.973416, + "lattice_table_mb": 2.956312, + "library_static_vertex_table": { + "build_ms": 280.04190000137896, + "cache_hit_us": 3.0999981390777975, + "rows": 46516, + "mb": 2.956312, + "equals_cell_lattice_table": true + } + } + }, + "device": { + "backend": "cpu", + "device": "cpu:0", + "hostname": "DESKTOP-H143S82", + "xla_flags": "--xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false --xla_disable_hlo_passes=constant_folding_probe_disabled", + "omp_num_threads": "1", + "cpu_count": 8, + "jax_compilation_cache_dir": null, + "autotune_cache_entries_at_start": 0, + "cache_fresh": false + }, + "jax_devices": [ + "cpu:0" + ], + "machine": { + "cpu_model": "Intel(R) Core(TM) i9-10885H CPU @ 2.40GHz", + "cpu_count_os": 8, + "nproc": 1, + "nproc_all": 8, + "nproc_note": "GNU nproc honours OMP_NUM_THREADS, so `nproc` can print 1 on a multi-core host; `nproc_all` is the core count.", + "ram_total": "16392692 kB", + "kernel": "5.10.16.3-microsoft-standard-WSL2", + "platform": "Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.35", + "python": "3.12.10", + "jax_thread_env": { + "NPROC": "8" + }, + "blas_thread_env": { + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + "VECLIB_MAXIMUM_THREADS": "1", + "NUMEXPR_NUM_THREADS": "1" + }, + "thread_env_note": "NPROC sizes XLA's CPU intra-op pool (the JAX rows); the BLAS knobs pin OpenBLAS/MKL/OpenMP (the numpy/scipy rows) and do not touch JAX. Two disjoint families; both are recorded on every CPU timing.", + "gpu": "NVIDIA GeForce RTX 2060 with Max-Q Design, 6144 MiB, 580.97" + }, + "thread_environment": { + "NPROC": "8", + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1" + }, + "xla_threads_observed_after_first_compile": { + "threads": 50, + "label": "simple_solved/control" + }, + "threads_at_end": 50, + "sched_affinity": 8, + "os_cpu_count": 8, + "xla_flags": "--xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false --xla_disable_hlo_passes=constant_folding_probe_disabled", + "jax_platforms": "cpu", + "jax_enable_x64": true, + "loadavg_start": [ + 5.64404296875, + 4.427734375, + 3.0703125 + ], + "loadavg_end": [ + 5.7548828125, + 4.25048828125, + 3.552734375 + ], + "protocol": { + "routes": [ + "control", + "exact", + "lattice", + "library" + ], + "n_rounds": 20, + "n_calls_per_round": 20, + "n_warm": 3, + "n_stream": 16, + "n_gate_instances": 3, + "vmap_batch": 4, + "instance_seed": 568, + "instance_stream": "entry 0 = prior medians; rest = vector_from_unit_vector(U(0.25,0.75))", + "argument": "physical parameter vector; instance_from_vector(xp=jnp) inside the trace", + "cache_trap_guard": "fresh closure + fresh solver/analyses per route; jax.clear_caches()", + "bootstrap": { + "samples": 2000, + "seed": 12345, + "interval": "90%" + }, + "gates": { + "log_likelihood_rtol": 1e-12, + "positions_atol": 1e-10, + "grad_rtol": 1e-10, + "grad_atol": 1e-12, + "vmap_rtol": 1e-12, + "cluster": "log-likelihood and positions only (no cluster grad/vmap/step-0 sets)" + }, + "pytrees": "autofit.jax.register_model(model) for every model before any trace" + }, + "free_parameters": { + "simple_solved": 5, + "simple_plain": 8, + "cluster_solved": 13, + "cluster_plain": 17 + }, + "model_configuration": { + "simple": { + "dataset": "dataset/point_source/simple/point_dataset_positions_only.json", + "solver_grid_shape": [ + 100, + 100 + ], + "solver_grid_pixel_scale": 0.2, + "solver_pixel_scale_precision": 0.001, + "magnification_threshold": 0.1, + "likelihoods": { + "solved": "FitPositionsImagePairAllSolved", + "plain": "FitPositionsImagePairAll" + } + }, + "cluster": { + "dataset": "dataset/cluster/simple/point_datasets.csv", + "n_systems": 2, + "n_mass_profiles": 13, + "solver_grid_shape": [ + 200, + 200 + ], + "solver_grid_pixel_scale": 0.7, + "solver_pixel_scale_precision": 0.01, + "source_centres_from": "back-traced observed-image centroids (cluster cell step 1)", + "likelihoods": { + "solved": "FitPositionsImagePairRepeatSolved (summed over systems)", + "plain": "FitPositionsImagePairRepeat (summed over systems)" + } + } + }, + "library_label": { + "per_row": { + "simple_solved": { + "library_step0_rows": [ + 11859 + ], + "expected_rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [ + "lattice" + ] + }, + "simple_solved_vmap4": { + "library_step0_rows": [ + 11859 + ], + "expected_rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [ + "lattice" + ] + }, + "simple_plain": { + "library_step0_rows": [ + 11859 + ], + "expected_rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [ + "lattice" + ] + }, + "cluster_solved": { + "library_step0_rows": [ + 46516 + ], + "expected_rows": { + "control": 276507, + "exact": 110085, + "lattice": 46516 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [] + }, + "cluster_plain": { + "library_step0_rows": [ + 46516 + ], + "expected_rows": { + "control": 276507, + "exact": 110085, + "lattice": 46516 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [] + } + }, + "library_matches": "lattice" + }, + "library_matches": "lattice", + "all_gates_pass": true, + "gate_summary": { + "simple_solved.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_plain.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_plain.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_plain.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_solved.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_solved.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_solved.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_plain.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_plain.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_plain.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_positions.control_vs_exact": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_positions.control_vs_lattice": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_positions.control_vs_library": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_grad.control_vs_exact": { + "pass": true, + "non_zero": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_grad.control_vs_lattice": { + "pass": true, + "non_zero": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_grad.control_vs_library": { + "pass": true, + "non_zero": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.control": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.exact": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.lattice": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.library": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_plain_step0_containing.control_vs_exact": { + "pass": true + }, + "simple_plain_step0_containing.control_vs_lattice": { + "pass": true + }, + "simple_plain_step0_containing.control_vs_library": { + "pass": true + }, + "cluster_solved_positions.control_vs_exact": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "cluster_solved_positions.control_vs_lattice": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "cluster_solved_positions.control_vs_library": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + } + }, + "rows": { + "simple_solved": { + "label": "simple_solved", + "batch": null, + "n_rounds": 20, + "n_calls_per_round": 20, + "n_warm": 3, + "route_order": "round-robin, start rotated each round", + "per_call_ms": { + "control": [ + 8.550200000172481, + 7.846699998481199, + 8.41500000024098, + 7.911000000603963, + 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z9ThR4;BJ5l$8qP*GGbbDXpZen%Tl!v6+?zOD@ID{i$KzU)Ue`R7ntTHAm))j(D1jn z;cU3Rkg*i`urE48RIa4a;Jx{v2$3&w5$!;avdzEk-nFX@n`1PJ9%M=E-Mcp(0u$5> zB}kn)6p^reUZQQ zhG*rIN4K*rdO|~l+WHmA33HG0{tTY f64[] {\n %constant.1 = f64[1000]{0} constant({...})\n %constant = f64[] constant(0)\n %wrapped_multiply = f64[1000]{0} fusion(%constant.1), kind=kLoop, calls=%wrapped_multiply_computation\n %wrapped_reduce-window = f64[32]{0} fusion(%wrapped_multiply, %constant), kind=kLoop, calls=%wrapped_reduce-window_computation\n ROOT %wrapped_reduce = f64[] fusion(%wrapped_reduce-window, %constant), kind=kLoop, calls=%wrapped_reduce_computation, metadata={op_name=\"jit()/dot_general\" stack_frame_id=3}\n}\n\n", + "agrees_with_request": true + } + }, + "library_versions": { + "autolens": "2026.8.17.1", + "autoarray": "2026.8.17.1", + "autofit": "2026.8.17.1" + }, + "package_versions": { + "jax": "0.10.2", + "jaxlib": "0.10.2" + }, + "source_revisions": { + "autolens_profiling": "3da2583d46b80f72793caf11a16433ca05f00d20", + "PyAutoNerves": "1fa613aa8d89a79613009fb66f5b4e2d21deb57a", + "PyAutoFit": "a736840127be7a3c21e15a5c945e94d8f1082983", + "PyAutoArray": "ad0bf97b45d572effa5d428a101d32ac4893b950", + "PyAutoGalaxy": "70a61e26cd715cefe7012a38285698091246a8a4", + "PyAutoLens": "b346b6a0a092098f23964e274edb220ea9757ad0" + }, + "autoarray_imported_from": "~/Code/PyAutoLabs-wt/point-source-cpu-p3/PyAutoArray", + "autolens_imported_from": "~/Code/PyAutoLabs-wt/point-source-cpu-p3/PyAutoLens", + "target": "autolens.point.solver.shape_solver.AbstractSolver._initial_triangles", + "route_bodies": { + "control": { + "name": "_control_initial_triangles", + "body": "def _control_initial_triangles(self, xp):\n \"\"\"The phase-2 main path: the flat (3N, 2) per-triangle table, no table attached.\"\"\"\n return _coordinate_array.CoordinateArrayTriangles.for_limits_and_scale(\n y_min=self.y_min, y_max=self.y_max, x_min=self.x_min, x_max=self.x_max, scale=self.scale\n )\n", + "bytecode_sha256": "decd881cb4e376c8" + }, + "table_factory": { + "name": "_table_initial_triangles", + "body": "def _table_initial_triangles(kind):\n def initial_triangles(self, xp):\n tables = _cell_tables(*_geometry(self))\n return _TableTriangles(\n coordinates=jnp.array(tables[\"coordinates\"]),\n side_length=self.scale,\n table=tables[kind],\n )\n\n initial_triangles.__name__ = f\"_{kind}_initial_triangles\"\n return initial_triangles\n", + "bytecode_sha256": "a522215e23585923" + }, + "tables": { + "name": "_cell_tables", + "body": "@functools.lru_cache(maxsize=16)\ndef _cell_tables(y_min, y_max, x_min, x_max, scale) -> dict:\n \"\"\"``{\"exact\": (V, I), \"lattice\": (V, I), \"coordinates\": C}`` for one geometry.\"\"\"\n t0 = time.perf_counter()\n coordinates = _lattice_coordinates(y_min, y_max, x_min, x_max, scale)\n flip = np.where((coordinates[:, 0] + coordinates[:, 1]) % 2 != 0, -1, 1)[:, None]\n scaling = np.array([0.5 * scale, HEIGHT_FACTOR * scale])\n centres = scaling * coordinates + np.array([0.0, 0.0])\n triangles = np.stack(\n (\n centres + flip * np.array([0.0, 0.5 * scale * HEIGHT_FACTOR]),\n centres + flip * np.array([0.5 * scale, -0.5 * scale * HEIGHT_FACTOR]),\n centres + flip * np.array([-0.5 * scale, -0.5 * scale * HEIGHT_FACTOR]),\n ),\n axis=1,\n )\n flat = triangles.reshape(-1, 2)\n\n exact_vertices, exact_inverse = np.unique(flat, axis=0, return_inverse=True)\n\n offsets = np.array([[0, 1], [1, -1], [-1, -1]])\n keys = np.stack(\n (\n coordinates[:, None, 0] + flip * offsets[None, :, 0],\n 2 * coordinates[:, None, 1] + flip * offsets[None, :, 1],\n ),\n axis=-1,\n ).reshape(-1, 2)\n _, first, lattice_inverse = np.unique(keys, axis=0, return_index=True, return_inverse=True)\n\n tables = {\n \"coordinates\": coordinates,\n \"exact\": (exact_vertices, exact_inverse.reshape(-1, 3)),\n \"lattice\": (flat[first].copy(), lattice_inverse.reshape(-1, 3)),\n }\n _TABLE_BUILD_S[(y_min, y_max, x_min, x_max, scale)] = time.perf_counter() - t0\n return tables\n", + "bytecode_sha256": "3b7bdf752e1994f7" + }, + "table_triangles": { + "body": "class _TableTriangles(_coordinate_array.CoordinateArrayTriangles):\n \"\"\"The library lattice with a cell-supplied ``(vertices, indices)`` table.\n\n Only ``_vertices_and_indices`` changes; every derived lattice is built as the base\n class by the library's own methods, so steps >= 1 are untouched.\n \"\"\"\n\n def __init__(self, coordinates, side_length, table):\n super().__init__(coordinates=coordinates, side_length=side_length)\n self._cell_table = table\n\n @property\n def _vertices_and_indices(self):\n vertices, indices = self._cell_table\n return jnp.asarray(vertices), jnp.asarray(indices)\n", + "bytecode_sha256": "6308d3338aa3791a" + } + }, + "body_provenance_captured": "import time", + "expected_step0_rows": { + "simple": { + "geometry": [ + -9.9, + 9.9, + -9.9, + 9.9, + 0.2 + ], + "n_triangles": 23283, + "rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + } + }, + "cluster": { + "geometry": [ + -69.64999999999999, + 69.64999999999999, + -69.64999999999999, + 69.64999999999999, + 0.7 + ], + "n_triangles": 92169, + "rows": { + "control": 276507, + "exact": 110085, + "lattice": 46516 + } + } + }, + "tables": { + "simple": { + "cell_build_ms": 122.63530000200262, + "cell_cache_hit_us": 1.0000003385357559, + "exact_table_mb": 1.017432, + "lattice_table_mb": 0.748536, + "library_static_vertex_table": { + "build_ms": 129.5334999995248, + "cache_hit_us": 2.5000008463393897, + "rows": 11859, + "mb": 0.748536, + "equals_cell_lattice_table": true + } + }, + "cluster": { + "cell_build_ms": 564.997500001482, + "cell_cache_hit_us": 0.9999967005569488, + "exact_table_mb": 3.973416, + "lattice_table_mb": 2.956312, + "library_static_vertex_table": { + "build_ms": 346.58109999872977, + "cache_hit_us": 2.4000000848900527, + "rows": 46516, + "mb": 2.956312, + "equals_cell_lattice_table": true + } + } + }, + "device": { + "backend": "cpu", + "device": "cpu:0", + "hostname": "DESKTOP-H143S82", + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false", + "omp_num_threads": "1", + "cpu_count": 8, + "jax_compilation_cache_dir": null, + "autotune_cache_entries_at_start": 0, + "cache_fresh": false + }, + "jax_devices": [ + "cpu:0" + ], + "machine": { + "cpu_model": "Intel(R) Core(TM) i9-10885H CPU @ 2.40GHz", + "cpu_count_os": 8, + "nproc": 1, + "nproc_all": 8, + "nproc_note": "GNU nproc honours OMP_NUM_THREADS, so `nproc` can print 1 on a multi-core host; `nproc_all` is the core count.", + "ram_total": "16392692 kB", + "kernel": "5.10.16.3-microsoft-standard-WSL2", + "platform": "Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.35", + "python": "3.12.10", + "jax_thread_env": { + "NPROC": "8" + }, + "blas_thread_env": { + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + "VECLIB_MAXIMUM_THREADS": "1", + "NUMEXPR_NUM_THREADS": "1" + }, + "thread_env_note": "NPROC sizes XLA's CPU intra-op pool (the JAX rows); the BLAS knobs pin OpenBLAS/MKL/OpenMP (the numpy/scipy rows) and do not touch JAX. Two disjoint families; both are recorded on every CPU timing.", + "gpu": "NVIDIA GeForce RTX 2060 with Max-Q Design, 6144 MiB, 580.97" + }, + "thread_environment": { + "NPROC": "8", + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1" + }, + "xla_threads_observed_after_first_compile": { + "threads": 42, + "label": "simple_solved/control" + }, + "threads_at_end": 42, + "sched_affinity": 8, + "os_cpu_count": 8, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false", + "jax_platforms": "cpu", + "jax_enable_x64": true, + "loadavg_start": [ + 2.07568359375, + 1.8193359375, + 1.59716796875 + ], + "loadavg_end": [ + 5.87451171875, + 4.4521484375, + 3.07080078125 + ], + "protocol": { + "routes": [ + "control", + "exact", + "lattice", + "library" + ], + "n_rounds": 20, + "n_calls_per_round": 20, + "n_warm": 3, + "n_stream": 16, + "n_gate_instances": 3, + "vmap_batch": 4, + "instance_seed": 568, + "instance_stream": "entry 0 = prior medians; rest = vector_from_unit_vector(U(0.25,0.75))", + "argument": "physical parameter vector; instance_from_vector(xp=jnp) inside the trace", + "cache_trap_guard": "fresh closure + fresh solver/analyses per route; jax.clear_caches()", + "bootstrap": { + "samples": 2000, + "seed": 12345, + "interval": "90%" + }, + "gates": { + "log_likelihood_rtol": 1e-12, + "positions_atol": 1e-10, + "grad_rtol": 1e-10, + "grad_atol": 1e-12, + "vmap_rtol": 1e-12, + "cluster": "log-likelihood and positions only (no cluster grad/vmap/step-0 sets)" + }, + "pytrees": "autofit.jax.register_model(model) for every model before any trace" + }, + "free_parameters": { + "simple_solved": 5, + "simple_plain": 8, + "cluster_solved": 13, + "cluster_plain": 17 + }, + "model_configuration": { + "simple": { + "dataset": "dataset/point_source/simple/point_dataset_positions_only.json", + "solver_grid_shape": [ + 100, + 100 + ], + "solver_grid_pixel_scale": 0.2, + "solver_pixel_scale_precision": 0.001, + "magnification_threshold": 0.1, + "likelihoods": { + "solved": "FitPositionsImagePairAllSolved", + "plain": "FitPositionsImagePairAll" + } + }, + "cluster": { + "dataset": "dataset/cluster/simple/point_datasets.csv", + "n_systems": 2, + "n_mass_profiles": 13, + "solver_grid_shape": [ + 200, + 200 + ], + "solver_grid_pixel_scale": 0.7, + "solver_pixel_scale_precision": 0.01, + "source_centres_from": "back-traced observed-image centroids (cluster cell step 1)", + "likelihoods": { + "solved": "FitPositionsImagePairRepeatSolved (summed over systems)", + "plain": "FitPositionsImagePairRepeat (summed over systems)" + } + } + }, + "library_label": { + "per_row": { + "simple_solved": { + "library_step0_rows": [ + 11859 + ], + "expected_rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [ + "lattice" + ] + }, + "simple_solved_vmap4": { + "library_step0_rows": [ + 11859 + ], + "expected_rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [ + "lattice" + ] + }, + "simple_plain": { + "library_step0_rows": [ + 11859 + ], + "expected_rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [ + "lattice" + ] + }, + "cluster_solved": { + "library_step0_rows": [ + 46516 + ], + "expected_rows": { + "control": 276507, + "exact": 110085, + "lattice": 46516 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [] + }, + "cluster_plain": { + "library_step0_rows": [ + 46516 + ], + "expected_rows": { + "control": 276507, + "exact": 110085, + "lattice": 46516 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [] + } + }, + "library_matches": "lattice" + }, + "library_matches": "lattice", + "all_gates_pass": true, + "gate_summary": { + "simple_solved.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_plain.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_plain.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_plain.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_solved.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_solved.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_solved.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_plain.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_plain.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_plain.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_positions.control_vs_exact": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_positions.control_vs_lattice": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_positions.control_vs_library": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_grad.control_vs_exact": { + "pass": true, + "non_zero": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_grad.control_vs_lattice": { + "pass": true, + "non_zero": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_grad.control_vs_library": { + "pass": true, + "non_zero": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.control": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.exact": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.lattice": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.library": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_plain_step0_containing.control_vs_exact": { + "pass": true + }, + "simple_plain_step0_containing.control_vs_lattice": { + "pass": true + }, + "simple_plain_step0_containing.control_vs_library": { + "pass": true + }, + "cluster_solved_positions.control_vs_exact": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "cluster_solved_positions.control_vs_lattice": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "cluster_solved_positions.control_vs_library": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + } + }, + "rows": { + "simple_solved": { + "label": "simple_solved", + "batch": null, + "n_rounds": 20, + "n_calls_per_round": 20, + "n_warm": 3, + "route_order": "round-robin, start rotated each round", + "per_call_ms": { + "control": [ + 7.857599997805664, + 9.398700000019744, + 7.893400001194095, + 6.959599999390775, + 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charset=UTF-8 Content-Transfer-Encoding: 8bit Pull the RAL JSONs/PNGs (CPU folding off + on, A100) and both job logs. All three JSONs import PyAutoArray ad0bf97b / PyAutoLens b346b6a0 from the branch clones, self-label library_matches=lattice, 31/31 gates bit-identical. CPU (Xeon 8490H, folding off) control -> library: simple solved 3.540 -> 1.758 ms (2.01x), vmap-4 1.43x, plain 2.08x, cluster 5.21x / 5.16x; compile +3..+12 %; XLA temp 4.82 -> 1.69 MB / 91.6 -> 22.6 MB. Folding on: 1.39-4.54x, worst compile +16.5 %. A100: 1.01-1.07x, compile -2..+13 %, no regression. Campaign note phase-3 section: RAL tables, gates, tie-case gate decision pending at ship, stop-rule verdict, phase-4 re-rank from FLOP shares. README phase-3 bullet updated. Co-Authored-By: Claude Opus 5.5 --- README.md | 14 +- ..._ab_constant_folding_hpc_ral_cpu_fp64.json | 13576 +++++++++++++++ ...e_ab_constant_folding_hpc_ral_cpu_fp64.png | Bin 0 -> 128737 bytes .../static_lattice_ab_hpc_ral_a100_fp64.json | 13577 ++++++++++++++++ .../static_lattice_ab_hpc_ral_a100_fp64.png | Bin 0 -> 148041 bytes .../static_lattice_ab_hpc_ral_cpu_fp64.json | 13576 +++++++++++++++ .../static_lattice_ab_hpc_ral_cpu_fp64.png | Bin 0 -> 127073 bytes ...09_24_ral_job_350636_static_lattice_ab.out | 294 + ..._ral_job_350637_static_lattice_ab_a100.out | 166 + results/notes/point_source_cpu_campaign.md | 206 +- 10 files changed, 41388 insertions(+), 21 deletions(-) create mode 100644 results/breakdown/point_source/static_lattice_ab_constant_folding_hpc_ral_cpu_fp64.json create mode 100644 results/breakdown/point_source/static_lattice_ab_constant_folding_hpc_ral_cpu_fp64.png create mode 100644 results/breakdown/point_source/static_lattice_ab_hpc_ral_a100_fp64.json create mode 100644 results/breakdown/point_source/static_lattice_ab_hpc_ral_a100_fp64.png create mode 100644 results/breakdown/point_source/static_lattice_ab_hpc_ral_cpu_fp64.json create mode 100644 results/breakdown/point_source/static_lattice_ab_hpc_ral_cpu_fp64.png create mode 100644 results/notes/point_source_cpu_2026_09_24_ral_job_350636_static_lattice_ab.out create mode 100644 results/notes/point_source_cpu_2026_09_24_ral_job_350637_static_lattice_ab_a100.out diff --git a/README.md b/README.md index 1ac8b817d..824fddd00 100644 --- a/README.md +++ b/README.md @@ -264,11 +264,15 @@ separately per likelihood × transform. Standing conclusions: (simple solved 1.70 → 0.84 ms, cluster 4.21 → 2.46 ms), every gate bit-identical. Accepted; the post-fix call is deflection-dominated (70 % of FLOPs), so phase 3 is the static initial-lattice precompute. Findings: [`results/notes/point_source_cpu_campaign.md`](./results/notes/point_source_cpu_campaign.md). -- **Point-source CPU speed-up, phase 3 (2026-09, in progress)** — precompute the JAX PointSolver's - static step-0 lattice: deflect the 11 859 geometrically unique vertices instead of the 69 849 flat - slots (PyAutoArray #568). Four-route A/B cell `static_lattice_ab.py`. Laptop witness only - (load-inflated, not quotable): simple 1.9×, cluster 2.7×, FLOPs −52 % / −72 %, every gate - bit-identical; compile-time effect unresolved on the laptop. RAL CPU (pinned) and A100 legs pending. +- **Point-source CPU speed-up, phase 3 (2026-09)** — precompute the JAX PointSolver's static step-0 + lattice: deflect the 11 859 geometrically unique vertices instead of the 69 849 flat slots + (46 516 vs 276 507 on the cluster; PyAutoArray #568, PyAutoLens). Four-route in-process A/B + `static_lattice_ab.py`, RAL job 350636 (`ral`, pinned Xeon 8490H, 8 CPUs, folding off): simple solved + 3.54 → 1.76 ms (**2.01×**), vmap-4 1.43×, two-source cluster 47.4 → 9.1 ms (**5.21×**); FLOPs −52 % / + −72 %, XLA temp 4.8 → 1.7 MB / 91.6 → 22.6 MB, compile +3–12 % (+17 % worst with constant folding on, + which gives 2.1–4.5× on the scalar rows). A100 (job 350637): no regression, 1.01–1.07× faster. All 31 gates bit-identical. + Accepted; a bit-exact source-on-vertex tie (control 3 images incl. a duplicate root vs lattice 2) is a + human gate decision at ship. Post-fix, the refinement-step deflections (≈ 60 % of simple FLOPs) lead phase 4. Findings: [`results/notes/point_source_cpu_campaign.md`](./results/notes/point_source_cpu_campaign.md). ## How to read this repo diff --git a/results/breakdown/point_source/static_lattice_ab_constant_folding_hpc_ral_cpu_fp64.json b/results/breakdown/point_source/static_lattice_ab_constant_folding_hpc_ral_cpu_fp64.json new file mode 100644 index 000000000..832d258a7 --- /dev/null +++ b/results/breakdown/point_source/static_lattice_ab_constant_folding_hpc_ral_cpu_fp64.json @@ -0,0 +1,13576 @@ +{ + "cell": "static_lattice_ab", + "issue": "PyAutoLabs/PyAutoArray#568", + "phase": 3, + "config_name": "hpc_ral_cpu_fp64", + "quotable": true, + "host_note": null, + "quick": false, + "models": [ + "simple", + "cluster" + ], + "constant_folding": { + "requested": true, + "xla_flags_requested": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false", + "xla_flags_effective": "--xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false --xla_disable_hlo_passes=constant_folding_probe_disabled", + "probe": { + "probe": "jit(lambda: c @ c), c = linspace(0.1, 1, 1000) constant", + "entry_has_computation": false, + "folding_ran": true, + "entry_hlo": "ENTRY %main.1 () -> f64[] {\n %constant.4 = f64[] constant(370.13513513513516), metadata={op_name=\"jit()/dot_general\" stack_frame_id=3}\n ROOT %copy = f64[] copy(%constant.4)\n}\n\n", + "agrees_with_request": true + } + }, + "library_versions": { + "autolens": "2026.8.17.1", + "autoarray": "2026.8.17.1", + "autofit": "2026.8.17.1" + }, + "package_versions": { + "jax": "0.10.2", + "jaxlib": "0.10.2" + }, + "source_revisions": { + "autolens_profiling": "3da2583d46b80f72793caf11a16433ca05f00d20", + "PyAutoNerves": "1fa613aa8d89a79613009fb66f5b4e2d21deb57a", + "PyAutoFit": "a736840127be7a3c21e15a5c945e94d8f1082983", + "PyAutoArray": "ad0bf97b45d572effa5d428a101d32ac4893b950", + "PyAutoGalaxy": "70a61e26cd715cefe7012a38285698091246a8a4", + "PyAutoLens": "b346b6a0a092098f23964e274edb220ea9757ad0" + }, + "autoarray_imported_from": "/mnt/ral/jnightin/autolens_profiling_wt/PyAutoArray_point-source-cpu-p3", + "autolens_imported_from": "/mnt/ral/jnightin/autolens_profiling_wt/PyAutoLens_point-source-cpu-p3", + "target": "autolens.point.solver.shape_solver.AbstractSolver._initial_triangles", + "route_bodies": { + "control": { + "name": "_control_initial_triangles", + "body": "def _control_initial_triangles(self, xp):\n \"\"\"The phase-2 main path: the flat (3N, 2) per-triangle table, no table attached.\"\"\"\n return _coordinate_array.CoordinateArrayTriangles.for_limits_and_scale(\n y_min=self.y_min, y_max=self.y_max, x_min=self.x_min, x_max=self.x_max, scale=self.scale\n )\n", + "bytecode_sha256": "decd881cb4e376c8" + }, + "table_factory": { + "name": "_table_initial_triangles", + "body": "def _table_initial_triangles(kind):\n def initial_triangles(self, xp):\n tables = _cell_tables(*_geometry(self))\n return _TableTriangles(\n coordinates=jnp.array(tables[\"coordinates\"]),\n side_length=self.scale,\n table=tables[kind],\n )\n\n initial_triangles.__name__ = f\"_{kind}_initial_triangles\"\n return initial_triangles\n", + "bytecode_sha256": "bbd192692915dcb2" + }, + "tables": { + "name": "_cell_tables", + "body": "@functools.lru_cache(maxsize=16)\ndef _cell_tables(y_min, y_max, x_min, x_max, scale) -> dict:\n \"\"\"``{\"exact\": (V, I), \"lattice\": (V, I), \"coordinates\": C}`` for one geometry.\"\"\"\n t0 = time.perf_counter()\n coordinates = _lattice_coordinates(y_min, y_max, x_min, x_max, scale)\n flip = np.where((coordinates[:, 0] + coordinates[:, 1]) % 2 != 0, -1, 1)[:, None]\n scaling = np.array([0.5 * scale, HEIGHT_FACTOR * scale])\n centres = scaling * coordinates + np.array([0.0, 0.0])\n triangles = np.stack(\n (\n centres + flip * np.array([0.0, 0.5 * scale * HEIGHT_FACTOR]),\n centres + flip * np.array([0.5 * scale, -0.5 * scale * HEIGHT_FACTOR]),\n centres + flip * np.array([-0.5 * scale, -0.5 * scale * HEIGHT_FACTOR]),\n ),\n axis=1,\n )\n flat = triangles.reshape(-1, 2)\n\n exact_vertices, exact_inverse = np.unique(flat, axis=0, return_inverse=True)\n\n offsets = np.array([[0, 1], [1, -1], [-1, -1]])\n keys = np.stack(\n (\n coordinates[:, None, 0] + flip * offsets[None, :, 0],\n 2 * coordinates[:, None, 1] + flip * offsets[None, :, 1],\n ),\n axis=-1,\n ).reshape(-1, 2)\n _, first, lattice_inverse = np.unique(keys, axis=0, return_index=True, return_inverse=True)\n\n tables = {\n \"coordinates\": coordinates,\n \"exact\": (exact_vertices, exact_inverse.reshape(-1, 3)),\n \"lattice\": (flat[first].copy(), lattice_inverse.reshape(-1, 3)),\n }\n _TABLE_BUILD_S[(y_min, y_max, x_min, x_max, scale)] = time.perf_counter() - t0\n return tables\n", + "bytecode_sha256": "3b7bdf752e1994f7" + }, + "table_triangles": { + "body": "class _TableTriangles(_coordinate_array.CoordinateArrayTriangles):\n \"\"\"The library lattice with a cell-supplied ``(vertices, indices)`` table.\n\n Only ``_vertices_and_indices`` changes; every derived lattice is built as the base\n class by the library's own methods, so steps >= 1 are untouched.\n \"\"\"\n\n def __init__(self, coordinates, side_length, table):\n super().__init__(coordinates=coordinates, side_length=side_length)\n self._cell_table = table\n\n @property\n def _vertices_and_indices(self):\n vertices, indices = self._cell_table\n return jnp.asarray(vertices), jnp.asarray(indices)\n", + "bytecode_sha256": "6308d3338aa3791a" + } + }, + "body_provenance_captured": "import time", + "expected_step0_rows": { + "simple": { + "geometry": [ + -9.9, + 9.9, + -9.9, + 9.9, + 0.2 + ], + "n_triangles": 23283, + "rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + } + }, + "cluster": { + "geometry": [ + -69.64999999999999, + 69.64999999999999, + -69.64999999999999, + 69.64999999999999, + 0.7 + ], + "n_triangles": 92169, + "rows": { + "control": 276507, + "exact": 110085, + "lattice": 46516 + } + } + }, + "tables": { + "simple": { + "cell_build_ms": 82.00608380138874, + "cell_cache_hit_us": 0.5490146577358246, + "exact_table_mb": 1.017432, + "lattice_table_mb": 0.748536, + "library_static_vertex_table": { + "build_ms": 34.38778826966882, + "cache_hit_us": 1.2917444109916687, + "rows": 11859, + "mb": 0.748536, + "equals_cell_lattice_table": true + } + }, + "cluster": { + "cell_build_ms": 395.868934225291, + "cell_cache_hit_us": 0.5341134965419769, + "exact_table_mb": 3.973416, + "lattice_table_mb": 2.956312, + "library_static_vertex_table": { + "build_ms": 181.9877908565104, + "cache_hit_us": 2.1061860024929047, + "rows": 46516, + "mb": 2.956312, + "equals_cell_lattice_table": true + } + } + }, + "device": { + "backend": "cpu", + "device": "cpu:0", + "hostname": "euclid-ral-compute-10-2", + "xla_flags": "--xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false --xla_disable_hlo_passes=constant_folding_probe_disabled", + "omp_num_threads": "1", + "cpu_count": 236, + "jax_compilation_cache_dir": null, + "autotune_cache_entries_at_start": 0, + "cache_fresh": false + }, + "jax_devices": [ + "cpu:0" + ], + "machine": { + "cpu_model": "Intel(R) Xeon(R) Platinum 8490H", + "cpu_count_os": 236, + "nproc": 1, + "nproc_all": 236, + "nproc_note": "GNU nproc honours OMP_NUM_THREADS, so `nproc` can print 1 on a multi-core host; `nproc_all` is the core count.", + "ram_total": "1010827868 kB", + "kernel": "5.14.0-687.39.1.el9_8.x86_64", + "platform": "Linux-5.14.0-687.39.1.el9_8.x86_64-x86_64-with-glibc2.34", + "python": "3.12.4", + "jax_thread_env": { + "NPROC": "8" + }, + "blas_thread_env": { + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + "VECLIB_MAXIMUM_THREADS": null, + "NUMEXPR_NUM_THREADS": null + }, + "thread_env_note": "NPROC sizes XLA's CPU intra-op pool (the JAX rows); the BLAS knobs pin OpenBLAS/MKL/OpenMP (the numpy/scipy rows) and do not touch JAX. Two disjoint families; both are recorded on every CPU timing.", + "gpu": null + }, + "thread_environment": { + "NPROC": "8", + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1" + }, + "xla_threads_observed_after_first_compile": { + "threads": 50, + "label": "simple_solved/control" + }, + "threads_at_end": 50, + "sched_affinity": 8, + "os_cpu_count": 236, + "xla_flags": "--xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false --xla_disable_hlo_passes=constant_folding_probe_disabled", + "jax_platforms": "cpu", + "jax_enable_x64": true, + "loadavg_start": [ + 3.06, + 1.68, + 0.9 + ], + "loadavg_end": [ + 1.58, + 1.49, + 1.17 + ], + "protocol": { + "routes": [ + "control", + "exact", + "lattice", + "library" + ], + "n_rounds": 20, + "n_calls_per_round": 20, + "n_warm": 3, + "n_stream": 16, + "n_gate_instances": 3, + "vmap_batch": 4, + "instance_seed": 568, + "instance_stream": "entry 0 = prior medians; rest = vector_from_unit_vector(U(0.25,0.75))", + "argument": "physical parameter vector; instance_from_vector(xp=jnp) inside the trace", + "cache_trap_guard": "fresh closure + fresh solver/analyses per route; jax.clear_caches()", + "bootstrap": { + "samples": 2000, + "seed": 12345, + "interval": "90%" + }, + "gates": { + "log_likelihood_rtol": 1e-12, + "positions_atol": 1e-10, + "grad_rtol": 1e-10, + "grad_atol": 1e-12, + "vmap_rtol": 1e-12, + "cluster": "log-likelihood and positions only (no cluster grad/vmap/step-0 sets)" + }, + "pytrees": "autofit.jax.register_model(model) for every model before any trace" + }, + "free_parameters": { + "simple_solved": 5, + "simple_plain": 8, + "cluster_solved": 13, + "cluster_plain": 17 + }, + "model_configuration": { + "simple": { + "dataset": "dataset/point_source/simple/point_dataset_positions_only.json", + "solver_grid_shape": [ + 100, + 100 + ], + "solver_grid_pixel_scale": 0.2, + "solver_pixel_scale_precision": 0.001, + "magnification_threshold": 0.1, + "likelihoods": { + "solved": "FitPositionsImagePairAllSolved", + "plain": "FitPositionsImagePairAll" + } + }, + "cluster": { + "dataset": "dataset/cluster/simple/point_datasets.csv", + "n_systems": 2, + "n_mass_profiles": 13, + "solver_grid_shape": [ + 200, + 200 + ], + "solver_grid_pixel_scale": 0.7, + "solver_pixel_scale_precision": 0.01, + "source_centres_from": "back-traced observed-image centroids (cluster cell step 1)", + "likelihoods": { + "solved": "FitPositionsImagePairRepeatSolved (summed over systems)", + "plain": "FitPositionsImagePairRepeat (summed over systems)" + } + } + }, + "library_label": { + "per_row": { + "simple_solved": { + "library_step0_rows": [ + 11859 + ], + "expected_rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [ + "lattice" + ] + }, + "simple_solved_vmap4": { + "library_step0_rows": [ + 11859 + ], + "expected_rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [ + "lattice" + ] + }, + "simple_plain": { + "library_step0_rows": [ + 11859 + ], + "expected_rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [ + "lattice" + ] + }, + "cluster_solved": { + "library_step0_rows": [ + 46516 + ], + "expected_rows": { + "control": 276507, + "exact": 110085, + "lattice": 46516 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [] + }, + "cluster_plain": { + "library_step0_rows": [ + 46516 + ], + "expected_rows": { + "control": 276507, + "exact": 110085, + "lattice": 46516 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [] + } + }, + "library_matches": "lattice" + }, + "library_matches": "lattice", + "all_gates_pass": true, + "gate_summary": { + "simple_solved.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_plain.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_plain.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_plain.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_solved.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_solved.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_solved.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_plain.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_plain.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_plain.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_positions.control_vs_exact": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_positions.control_vs_lattice": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_positions.control_vs_library": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_grad.control_vs_exact": { + "pass": true, + "non_zero": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_grad.control_vs_lattice": { + "pass": true, + "non_zero": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_grad.control_vs_library": { + "pass": true, + "non_zero": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.control": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.exact": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.lattice": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.library": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_plain_step0_containing.control_vs_exact": { + "pass": true + }, + "simple_plain_step0_containing.control_vs_lattice": { + "pass": true + }, + "simple_plain_step0_containing.control_vs_library": { + "pass": true + }, + "cluster_solved_positions.control_vs_exact": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "cluster_solved_positions.control_vs_lattice": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "cluster_solved_positions.control_vs_library": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + } + }, + "rows": { + "simple_solved": { + "label": "simple_solved", + "batch": null, + "n_rounds": 20, + "n_calls_per_round": 20, + "n_warm": 3, + "route_order": "round-robin, start rotated each round", + "per_call_ms": { + "control": [ + 3.7937909364700317, + 4.846933763474226, + 4.181128926575184, + 3.7395008839666843, + 3.1621139496564865, + 3.2064998522400856, + 4.3112002313137054, + 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metadata={op_name=\"jit()/dot_general\" stack_frame_id=3}\n}\n\n", + "agrees_with_request": true + } + }, + "library_versions": { + "autolens": "2026.8.17.1", + "autoarray": "2026.8.17.1", + "autofit": "2026.8.17.1" + }, + "package_versions": { + "jax": "0.10.2", + "jaxlib": "0.10.2" + }, + "source_revisions": { + "autolens_profiling": "3da2583d46b80f72793caf11a16433ca05f00d20", + "PyAutoNerves": "1fa613aa8d89a79613009fb66f5b4e2d21deb57a", + "PyAutoFit": "a736840127be7a3c21e15a5c945e94d8f1082983", + "PyAutoArray": "ad0bf97b45d572effa5d428a101d32ac4893b950", + "PyAutoGalaxy": "70a61e26cd715cefe7012a38285698091246a8a4", + "PyAutoLens": "b346b6a0a092098f23964e274edb220ea9757ad0" + }, + "autoarray_imported_from": "/mnt/ral/jnightin/autolens_profiling_wt/PyAutoArray_point-source-cpu-p3", + "autolens_imported_from": "/mnt/ral/jnightin/autolens_profiling_wt/PyAutoLens_point-source-cpu-p3", + "target": "autolens.point.solver.shape_solver.AbstractSolver._initial_triangles", + "route_bodies": { + "control": { + "name": "_control_initial_triangles", + "body": "def _control_initial_triangles(self, xp):\n \"\"\"The phase-2 main path: the flat (3N, 2) per-triangle table, no table attached.\"\"\"\n return _coordinate_array.CoordinateArrayTriangles.for_limits_and_scale(\n y_min=self.y_min, y_max=self.y_max, x_min=self.x_min, x_max=self.x_max, scale=self.scale\n )\n", + "bytecode_sha256": "decd881cb4e376c8" + }, + "table_factory": { + "name": "_table_initial_triangles", + "body": "def _table_initial_triangles(kind):\n def initial_triangles(self, xp):\n tables = _cell_tables(*_geometry(self))\n return _TableTriangles(\n coordinates=jnp.array(tables[\"coordinates\"]),\n side_length=self.scale,\n table=tables[kind],\n )\n\n initial_triangles.__name__ = f\"_{kind}_initial_triangles\"\n return initial_triangles\n", + "bytecode_sha256": "4792d616de56e496" + }, + "tables": { + "name": "_cell_tables", + "body": "@functools.lru_cache(maxsize=16)\ndef _cell_tables(y_min, y_max, x_min, x_max, scale) -> dict:\n \"\"\"``{\"exact\": (V, I), \"lattice\": (V, I), \"coordinates\": C}`` for one geometry.\"\"\"\n t0 = time.perf_counter()\n coordinates = _lattice_coordinates(y_min, y_max, x_min, x_max, scale)\n flip = np.where((coordinates[:, 0] + coordinates[:, 1]) % 2 != 0, -1, 1)[:, None]\n scaling = np.array([0.5 * scale, HEIGHT_FACTOR * scale])\n centres = scaling * coordinates + np.array([0.0, 0.0])\n triangles = np.stack(\n (\n centres + flip * np.array([0.0, 0.5 * scale * HEIGHT_FACTOR]),\n centres + flip * np.array([0.5 * scale, -0.5 * scale * HEIGHT_FACTOR]),\n centres + flip * np.array([-0.5 * scale, -0.5 * scale * HEIGHT_FACTOR]),\n ),\n axis=1,\n )\n flat = triangles.reshape(-1, 2)\n\n exact_vertices, exact_inverse = np.unique(flat, axis=0, return_inverse=True)\n\n offsets = np.array([[0, 1], [1, -1], [-1, -1]])\n keys = np.stack(\n (\n coordinates[:, None, 0] + flip * offsets[None, :, 0],\n 2 * coordinates[:, None, 1] + flip * offsets[None, :, 1],\n ),\n axis=-1,\n ).reshape(-1, 2)\n _, first, lattice_inverse = np.unique(keys, axis=0, return_index=True, return_inverse=True)\n\n tables = {\n \"coordinates\": coordinates,\n \"exact\": (exact_vertices, exact_inverse.reshape(-1, 3)),\n \"lattice\": (flat[first].copy(), lattice_inverse.reshape(-1, 3)),\n }\n _TABLE_BUILD_S[(y_min, y_max, x_min, x_max, scale)] = time.perf_counter() - t0\n return tables\n", + "bytecode_sha256": "3b7bdf752e1994f7" + }, + "table_triangles": { + "body": "class _TableTriangles(_coordinate_array.CoordinateArrayTriangles):\n \"\"\"The library lattice with a cell-supplied ``(vertices, indices)`` table.\n\n Only ``_vertices_and_indices`` changes; every derived lattice is built as the base\n class by the library's own methods, so steps >= 1 are untouched.\n \"\"\"\n\n def __init__(self, coordinates, side_length, table):\n super().__init__(coordinates=coordinates, side_length=side_length)\n self._cell_table = table\n\n @property\n def _vertices_and_indices(self):\n vertices, indices = self._cell_table\n return jnp.asarray(vertices), jnp.asarray(indices)\n", + "bytecode_sha256": "6308d3338aa3791a" + } + }, + "body_provenance_captured": "import time", + "expected_step0_rows": { + "simple": { + "geometry": [ + -9.9, + 9.9, + -9.9, + 9.9, + 0.2 + ], + "n_triangles": 23283, + "rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + } + }, + "cluster": { + "geometry": [ + -69.64999999999999, + 69.64999999999999, + -69.64999999999999, + 69.64999999999999, + 0.7 + ], + "n_triangles": 92169, + "rows": { + "control": 276507, + "exact": 110085, + "lattice": 46516 + } + } + }, + "tables": { + "simple": { + "cell_build_ms": 185.3060140274465, + "cell_cache_hit_us": 0.6495974957942963, + "exact_table_mb": 1.017432, + "lattice_table_mb": 0.748536, + "library_static_vertex_table": { + "build_ms": 47.864540945738554, + "cache_hit_us": 1.9799917936325073, + "rows": 11859, + "mb": 0.748536, + "equals_cell_lattice_table": true + } + }, + "cluster": { + 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multi-core host; `nproc_all` is the core count.", + "ram_total": "928298232 kB", + "kernel": "5.14.0-687.39.1.el9_8.x86_64", + "platform": "Linux-5.14.0-687.39.1.el9_8.x86_64-x86_64-with-glibc2.34", + "python": "3.12.4", + "jax_thread_env": { + "NPROC": "8" + }, + "blas_thread_env": { + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + "VECLIB_MAXIMUM_THREADS": null, + "NUMEXPR_NUM_THREADS": null + }, + "thread_env_note": "NPROC sizes XLA's CPU intra-op pool (the JAX rows); the BLAS knobs pin OpenBLAS/MKL/OpenMP (the numpy/scipy rows) and do not touch JAX. 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b/results/breakdown/point_source/static_lattice_ab_hpc_ral_cpu_fp64.json new file mode 100644 index 000000000..4f770a4f0 --- /dev/null +++ b/results/breakdown/point_source/static_lattice_ab_hpc_ral_cpu_fp64.json @@ -0,0 +1,13576 @@ +{ + "cell": "static_lattice_ab", + "issue": "PyAutoLabs/PyAutoArray#568", + "phase": 3, + "config_name": "hpc_ral_cpu_fp64", + "quotable": true, + "host_note": null, + "quick": false, + "models": [ + "simple", + "cluster" + ], + "constant_folding": { + "requested": false, + "xla_flags_requested": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false", + "xla_flags_effective": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false", + "probe": { + "probe": "jit(lambda: c @ c), c = linspace(0.1, 1, 1000) constant", + "entry_has_computation": true, + "folding_ran": false, + "entry_hlo": "ENTRY %main.1 () -> f64[] {\n %constant.1 = f64[1000]{0} constant({...})\n %constant = f64[] constant(0)\n %wrapped_multiply = f64[1000]{0} fusion(%constant.1), kind=kLoop, calls=%wrapped_multiply_computation\n %wrapped_reduce-window = f64[32]{0} fusion(%wrapped_multiply, %constant), kind=kLoop, calls=%wrapped_reduce-window_computation\n ROOT %wrapped_reduce = f64[] fusion(%wrapped_reduce-window, %constant), kind=kLoop, calls=%wrapped_reduce_computation, metadata={op_name=\"jit()/dot_general\" stack_frame_id=3}\n}\n\n", + "agrees_with_request": true + } + }, + "library_versions": { + "autolens": "2026.8.17.1", + "autoarray": "2026.8.17.1", + "autofit": "2026.8.17.1" + }, + "package_versions": { + "jax": "0.10.2", + "jaxlib": "0.10.2" + }, + "source_revisions": { + "autolens_profiling": "3da2583d46b80f72793caf11a16433ca05f00d20", + "PyAutoNerves": "1fa613aa8d89a79613009fb66f5b4e2d21deb57a", + "PyAutoFit": "a736840127be7a3c21e15a5c945e94d8f1082983", + "PyAutoArray": "ad0bf97b45d572effa5d428a101d32ac4893b950", + "PyAutoGalaxy": "70a61e26cd715cefe7012a38285698091246a8a4", + "PyAutoLens": "b346b6a0a092098f23964e274edb220ea9757ad0" + }, + "autoarray_imported_from": "/mnt/ral/jnightin/autolens_profiling_wt/PyAutoArray_point-source-cpu-p3", + "autolens_imported_from": "/mnt/ral/jnightin/autolens_profiling_wt/PyAutoLens_point-source-cpu-p3", + "target": "autolens.point.solver.shape_solver.AbstractSolver._initial_triangles", + "route_bodies": { + "control": { + "name": "_control_initial_triangles", + "body": "def _control_initial_triangles(self, xp):\n \"\"\"The phase-2 main path: the flat (3N, 2) per-triangle table, no table attached.\"\"\"\n return _coordinate_array.CoordinateArrayTriangles.for_limits_and_scale(\n y_min=self.y_min, y_max=self.y_max, x_min=self.x_min, x_max=self.x_max, scale=self.scale\n )\n", + "bytecode_sha256": "decd881cb4e376c8" + }, + "table_factory": { + "name": "_table_initial_triangles", + "body": "def _table_initial_triangles(kind):\n def initial_triangles(self, xp):\n tables = _cell_tables(*_geometry(self))\n return _TableTriangles(\n coordinates=jnp.array(tables[\"coordinates\"]),\n side_length=self.scale,\n table=tables[kind],\n )\n\n initial_triangles.__name__ = f\"_{kind}_initial_triangles\"\n return initial_triangles\n", + "bytecode_sha256": "90de00bd5a86b962" + }, + "tables": { + "name": "_cell_tables", + "body": "@functools.lru_cache(maxsize=16)\ndef _cell_tables(y_min, y_max, x_min, x_max, scale) -> dict:\n \"\"\"``{\"exact\": (V, I), \"lattice\": (V, I), \"coordinates\": C}`` for one geometry.\"\"\"\n t0 = time.perf_counter()\n coordinates = _lattice_coordinates(y_min, y_max, x_min, x_max, scale)\n flip = np.where((coordinates[:, 0] + coordinates[:, 1]) % 2 != 0, -1, 1)[:, None]\n scaling = np.array([0.5 * scale, HEIGHT_FACTOR * scale])\n centres = scaling * coordinates + np.array([0.0, 0.0])\n triangles = np.stack(\n (\n centres + flip * np.array([0.0, 0.5 * scale * HEIGHT_FACTOR]),\n centres + flip * np.array([0.5 * scale, -0.5 * scale * HEIGHT_FACTOR]),\n centres + flip * np.array([-0.5 * scale, -0.5 * scale * HEIGHT_FACTOR]),\n ),\n axis=1,\n )\n flat = triangles.reshape(-1, 2)\n\n exact_vertices, exact_inverse = np.unique(flat, axis=0, return_inverse=True)\n\n offsets = np.array([[0, 1], [1, -1], [-1, -1]])\n keys = np.stack(\n (\n coordinates[:, None, 0] + flip * offsets[None, :, 0],\n 2 * coordinates[:, None, 1] + flip * offsets[None, :, 1],\n ),\n axis=-1,\n ).reshape(-1, 2)\n _, first, lattice_inverse = np.unique(keys, axis=0, return_index=True, return_inverse=True)\n\n tables = {\n \"coordinates\": coordinates,\n \"exact\": (exact_vertices, exact_inverse.reshape(-1, 3)),\n \"lattice\": (flat[first].copy(), lattice_inverse.reshape(-1, 3)),\n }\n _TABLE_BUILD_S[(y_min, y_max, x_min, x_max, scale)] = time.perf_counter() - t0\n return tables\n", + "bytecode_sha256": "3b7bdf752e1994f7" + }, + "table_triangles": { + "body": "class _TableTriangles(_coordinate_array.CoordinateArrayTriangles):\n \"\"\"The library lattice with a cell-supplied ``(vertices, indices)`` table.\n\n Only ``_vertices_and_indices`` changes; every derived lattice is built as the base\n class by the library's own methods, so steps >= 1 are untouched.\n \"\"\"\n\n def __init__(self, coordinates, side_length, table):\n super().__init__(coordinates=coordinates, side_length=side_length)\n self._cell_table = table\n\n @property\n def _vertices_and_indices(self):\n vertices, indices = self._cell_table\n return jnp.asarray(vertices), jnp.asarray(indices)\n", + "bytecode_sha256": "6308d3338aa3791a" + } + }, + "body_provenance_captured": "import time", + "expected_step0_rows": { + "simple": { + "geometry": [ + -9.9, + 9.9, + -9.9, + 9.9, + 0.2 + ], + "n_triangles": 23283, + "rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + } + }, + "cluster": { + "geometry": [ + -69.64999999999999, + 69.64999999999999, + -69.64999999999999, + 69.64999999999999, + 0.7 + ], + "n_triangles": 92169, + "rows": { + "control": 276507, + "exact": 110085, + "lattice": 46516 + } + } + }, + "tables": { + "simple": { + "cell_build_ms": 129.0445444174111, + "cell_cache_hit_us": 0.6402842700481415, + "exact_table_mb": 1.017432, + "lattice_table_mb": 0.748536, + "library_static_vertex_table": { + "build_ms": 34.097347874194384, + "cache_hit_us": 1.2139789760112762, + "rows": 11859, + "mb": 0.748536, + "equals_cell_lattice_table": true + } + }, + "cluster": { + "cell_build_ms": 402.48990105465055, + "cell_cache_hit_us": 0.5490146577358246, + "exact_table_mb": 3.973416, + "lattice_table_mb": 2.956312, + "library_static_vertex_table": { + "build_ms": 184.25948917865753, + "cache_hit_us": 2.3706816136837006, + "rows": 46516, + "mb": 2.956312, + "equals_cell_lattice_table": true + } + } + }, + "device": { + "backend": "cpu", + "device": "cpu:0", + "hostname": "euclid-ral-compute-10-2", + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false", + "omp_num_threads": "1", + "cpu_count": 236, + "jax_compilation_cache_dir": null, + "autotune_cache_entries_at_start": 0, + "cache_fresh": false + }, + "jax_devices": [ + "cpu:0" + ], + "machine": { + "cpu_model": "Intel(R) Xeon(R) Platinum 8490H", + "cpu_count_os": 236, + "nproc": 1, + "nproc_all": 236, + "nproc_note": "GNU nproc honours OMP_NUM_THREADS, so `nproc` can print 1 on a multi-core host; `nproc_all` is the core count.", + "ram_total": "1010827868 kB", + "kernel": "5.14.0-687.39.1.el9_8.x86_64", + "platform": "Linux-5.14.0-687.39.1.el9_8.x86_64-x86_64-with-glibc2.34", + "python": "3.12.4", + "jax_thread_env": { + "NPROC": "8" + }, + "blas_thread_env": { + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + "VECLIB_MAXIMUM_THREADS": null, + "NUMEXPR_NUM_THREADS": null + }, + "thread_env_note": "NPROC sizes XLA's CPU intra-op pool (the JAX rows); the BLAS knobs pin OpenBLAS/MKL/OpenMP (the numpy/scipy rows) and do not touch JAX. Two disjoint families; both are recorded on every CPU timing.", + "gpu": null + }, + "thread_environment": { + "NPROC": "8", + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1" + }, + "xla_threads_observed_after_first_compile": { + "threads": 42, + "label": "simple_solved/control" + }, + "threads_at_end": 42, + "sched_affinity": 8, + "os_cpu_count": 236, + "xla_flags": "--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false", + "jax_platforms": "cpu", + "jax_enable_x64": true, + "loadavg_start": [ + 0.28, + 0.07, + 0.02 + ], + "loadavg_end": [ + 3.06, + 1.68, + 0.9 + ], + "protocol": { + "routes": [ + "control", + "exact", + "lattice", + "library" + ], + "n_rounds": 20, + "n_calls_per_round": 20, + "n_warm": 3, + "n_stream": 16, + "n_gate_instances": 3, + "vmap_batch": 4, + "instance_seed": 568, + "instance_stream": "entry 0 = prior medians; rest = vector_from_unit_vector(U(0.25,0.75))", + "argument": "physical parameter vector; instance_from_vector(xp=jnp) inside the trace", + "cache_trap_guard": "fresh closure + fresh solver/analyses per route; jax.clear_caches()", + "bootstrap": { + "samples": 2000, + "seed": 12345, + "interval": "90%" + }, + "gates": { + "log_likelihood_rtol": 1e-12, + "positions_atol": 1e-10, + "grad_rtol": 1e-10, + "grad_atol": 1e-12, + "vmap_rtol": 1e-12, + "cluster": "log-likelihood and positions only (no cluster grad/vmap/step-0 sets)" + }, + "pytrees": "autofit.jax.register_model(model) for every model before any trace" + }, + "free_parameters": { + "simple_solved": 5, + "simple_plain": 8, + "cluster_solved": 13, + "cluster_plain": 17 + }, + "model_configuration": { + "simple": { + "dataset": "dataset/point_source/simple/point_dataset_positions_only.json", + "solver_grid_shape": [ + 100, + 100 + ], + "solver_grid_pixel_scale": 0.2, + "solver_pixel_scale_precision": 0.001, + "magnification_threshold": 0.1, + "likelihoods": { + "solved": "FitPositionsImagePairAllSolved", + "plain": "FitPositionsImagePairAll" + } + }, + "cluster": { + "dataset": "dataset/cluster/simple/point_datasets.csv", + "n_systems": 2, + "n_mass_profiles": 13, + "solver_grid_shape": [ + 200, + 200 + ], + "solver_grid_pixel_scale": 0.7, + "solver_pixel_scale_precision": 0.01, + "source_centres_from": "back-traced observed-image centroids (cluster cell step 1)", + "likelihoods": { + "solved": "FitPositionsImagePairRepeatSolved (summed over systems)", + "plain": "FitPositionsImagePairRepeat (summed over systems)" + } + } + }, + "library_label": { + "per_row": { + "simple_solved": { + "library_step0_rows": [ + 11859 + ], + "expected_rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [ + "lattice" + ] + }, + "simple_solved_vmap4": { + "library_step0_rows": [ + 11859 + ], + "expected_rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [ + "lattice" + ] + }, + "simple_plain": { + "library_step0_rows": [ + 11859 + ], + "expected_rows": { + "control": 69849, + "exact": 28665, + "lattice": 11859 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [ + "lattice" + ] + }, + "cluster_solved": { + "library_step0_rows": [ + 46516 + ], + "expected_rows": { + "control": 276507, + "exact": 110085, + "lattice": 46516 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [] + }, + "cluster_plain": { + "library_step0_rows": [ + 46516 + ], + "expected_rows": { + "control": 276507, + "exact": 110085, + "lattice": 46516 + }, + "library_matches": "lattice", + "library_hlo_identical_to": [] + } + }, + "library_matches": "lattice" + }, + "library_matches": "lattice", + "all_gates_pass": true, + "gate_summary": { + "simple_solved.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_plain.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_plain.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_plain.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_solved.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_solved.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_solved.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_plain.log_likelihood.control_vs_exact": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_plain.log_likelihood.control_vs_lattice": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "cluster_plain.log_likelihood.control_vs_library": { + "pass": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_positions.control_vs_exact": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_positions.control_vs_lattice": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_positions.control_vs_library": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_grad.control_vs_exact": { + "pass": true, + "non_zero": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_grad.control_vs_lattice": { + "pass": true, + "non_zero": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_grad.control_vs_library": { + "pass": true, + "non_zero": true, + "bit_identical": true, + "max_rel_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.control": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.exact": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.lattice": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_solved_vmap4.vmap_vs_scalar.library": { + "pass": true, + "bit_identical": true, + "max_abs_delta": 0.0 + }, + "simple_plain_step0_containing.control_vs_exact": { + "pass": true + }, + "simple_plain_step0_containing.control_vs_lattice": { + "pass": true + }, + "simple_plain_step0_containing.control_vs_library": { + "pass": true + }, + "cluster_solved_positions.control_vs_exact": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "cluster_solved_positions.control_vs_lattice": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + }, + "cluster_solved_positions.control_vs_library": { + "pass": true, + "image_counts_identical": true, + "max_abs_delta": 0.0 + } + }, + "rows": { + "simple_solved": { + "label": "simple_solved", + "batch": null, + "n_rounds": 20, + "n_calls_per_round": 20, + "n_warm": 3, + "route_order": "round-robin, start rotated each round", + "per_call_ms": { + "control": [ + 3.336119931191206, + 3.570845816284418, + 3.6327880807220936, + 3.7424233742058277, + 3.6326879635453224, + 4.094996023923159, + 3.624912817031145, + 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b/results/notes/point_source_cpu_2026_09_24_ral_job_350636_static_lattice_ab.out @@ -0,0 +1,294 @@ +AP_ROOT=/mnt/ral/jnightin/autolens_profiling_wt/point-source-cpu-p3 +========================================== +Thu 24 Sep 13:34:42 BST 2026 +euclid-ral-compute-10-2 +Model name: Intel(R) Xeon(R) Platinum 8490H +partition: ral +nodelist: euclid-ral-compute-10-2 +cpus-per-task: 8 +sched_affinity: 8 +loadavg: 0.00 0.00 0.00 1/2096 1665220 +Cell: point_source/likelihood_breakdown/static_lattice_ab +Precision: fp64 (JAX_ENABLE_X64=1) +Thread env: OMP=1 MKL=1 OPENBLAS=1 NPROC=8 +XLA_FLAGS: --xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false +=== source revisions === +PyAutoNerves (shared) 1fa613aa8d89a79613009fb66f5b4e2d21deb57a +PyAutoFit (shared) a736840127be7a3c21e15a5c945e94d8f1082983 +PyAutoArray (shared) 11b93476b2c7d9d32e5467deedfe9594c637d736 +PyAutoGalaxy (shared) 70a61e26cd715cefe7012a38285698091246a8a4 +PyAutoLens (shared) 2aaa1c1a8bb4a5923edf30a18451fcea330a6bd4 +PyAutoArray (branch clone, IMPORTED): ad0bf97b45d572effa5d428a101d32ac4893b950 +PyAutoLens (branch clone, IMPORTED): b346b6a0a092098f23964e274edb220ea9757ad0 +AP_ROOT rev: 3da2583d46b80f72793caf11a16433ca05f00d20 +AP_ROOT branch: feature/point-source-cpu-p3 +=== import provenance (refuses unless both libraries come from the branch clones) === +autoarray.__file__ = /mnt/ral/jnightin/autolens_profiling_wt/PyAutoArray_point-source-cpu-p3/autoarray/__init__.py +autolens.__file__ = /mnt/ral/jnightin/autolens_profiling_wt/PyAutoLens_point-source-cpu-p3/autolens/__init__.py +autofit.__file__ = /mnt/ral/jnightin/PyAuto/PyAutoFit/autofit/__init__.py +autogalaxy.__file__ = /mnt/ral/jnightin/PyAuto/PyAutoGalaxy/autogalaxy/__init__.py +jax 0.10.2 [CpuDevice(id=0)] +ASSERT OK: autoarray + autolens from the branch clones; static_vertex_table present +========================================== +run 1/2: constant folding DISABLED (20 x 20, simple + cluster) +Thu 24 Sep 13:34:59 BST 2026 +loadavg: 0.28 0.07 0.02 1/2098 1665268 +2026-09-24 13:35:10,444 - matplotlib.font_manager - INFO - generated new fontManager +constant folding: requested=disabled probe folding_ran=False XLA_FLAGS=--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false + +=== simple: step-0 rows {'control': 69849, 'exact': 28665, 'lattice': 11859}; tables {'cell_build_ms': 129.0445444174111, 'cell_cache_hit_us': 0.6402842700481415, 'exact_table_mb': 1.017432, 'lattice_table_mb': 0.748536, 'library_static_vertex_table': {'build_ms': 34.097347874194384, 'cache_hit_us': 1.2139789760112762, 'rows': 11859, 'mb': 0.748536, 'equals_cell_lattice_table': True}} + +--- row simple_solved --- + [simple_solved/control] lower 2.0 s compile 2.9 s first 8.63 ms trace_calls 2 step0_rows [69849] + [simple_solved/exact] lower 1.9 s compile 3.0 s first 3.21 ms trace_calls 2 step0_rows [28665] + [simple_solved/lattice] lower 1.8 s compile 3.1 s first 3.43 ms trace_calls 2 step0_rows [11859] + [simple_solved/library] lower 1.9 s compile 3.1 s first 2.78 ms trace_calls 2 step0_rows [11859] + median ms control 3.540 exact 2.254 lattice 1.674 library 1.758 control/lattice 2.114 [1.917, 2.156] + +--- positions gate simple_solved --- + [simple_solved_positions/control] lower 1.1 s compile 1.5 s first 3.87 ms trace_calls 1 step0_rows [69849] + [simple_solved_positions/exact] lower 1.1 s compile 1.5 s first 3.39 ms trace_calls 1 step0_rows [28665] + [simple_solved_positions/lattice] lower 1.0 s compile 1.5 s first 2.41 ms trace_calls 1 step0_rows [11859] + [simple_solved_positions/library] lower 1.1 s compile 1.5 s first 2.81 ms trace_calls 1 step0_rows [11859] + +--- gradient gate simple_solved --- + [simple_solved_grad/control] lower 3.4 s compile 4.6 s first 5.99 ms trace_calls 2 step0_rows [69849] + [simple_solved_grad/exact] lower 3.3 s compile 4.8 s first 3.95 ms trace_calls 2 step0_rows [28665] + [simple_solved_grad/lattice] lower 3.4 s compile 4.9 s first 3.79 ms trace_calls 2 step0_rows [11859] + [simple_solved_grad/library] lower 3.4 s compile 4.9 s first 4.10 ms trace_calls 2 step0_rows [11859] + +--- row simple_solved_vmap4 --- + [simple_solved_vmap4/control] lower 2.7 s compile 3.4 s first 12.38 ms trace_calls 2 step0_rows [69849] + [simple_solved_vmap4/exact] lower 2.7 s compile 3.5 s first 8.90 ms trace_calls 2 step0_rows [28665] + [simple_solved_vmap4/lattice] lower 2.6 s compile 3.7 s first 5.31 ms trace_calls 2 step0_rows [11859] + [simple_solved_vmap4/library] lower 2.7 s compile 3.7 s first 7.63 ms trace_calls 2 step0_rows [11859] + median ms control 8.258 exact 6.550 lattice 5.761 library 5.784 control/lattice 1.433 [1.421, 1.456] + +--- row simple_plain --- + [simple_plain/control] lower 1.5 s compile 2.6 s first 3.77 ms trace_calls 2 step0_rows [69849] + [simple_plain/exact] lower 1.4 s compile 2.7 s first 3.34 ms trace_calls 2 step0_rows [28665] + [simple_plain/lattice] lower 1.5 s compile 2.6 s first 2.85 ms trace_calls 2 step0_rows [11859] + [simple_plain/library] lower 1.5 s compile 2.7 s first 2.91 ms trace_calls 2 step0_rows [11859] + median ms control 3.479 exact 2.267 lattice 1.673 library 1.669 control/lattice 2.080 [1.893, 2.129] + +--- step-0 containing_indices gate simple_plain --- + [simple_plain_step0/control] lower 0.2 s compile 0.2 s first 4.16 ms trace_calls 1 step0_rows [69849] + [simple_plain_step0/exact] lower 0.1 s compile 0.2 s first 2.44 ms trace_calls 1 step0_rows [28665] + [simple_plain_step0/lattice] lower 0.1 s compile 0.2 s first 1.98 ms trace_calls 1 step0_rows [11859] + [simple_plain_step0/library] lower 0.2 s compile 0.2 s first 1.99 ms trace_calls 1 step0_rows [11859] + +=== cluster: step-0 rows {'control': 276507, 'exact': 110085, 'lattice': 46516}; tables {'cell_build_ms': 402.48990105465055, 'cell_cache_hit_us': 0.5490146577358246, 'exact_table_mb': 3.973416, 'lattice_table_mb': 2.956312, 'library_static_vertex_table': {'build_ms': 184.25948917865753, 'cache_hit_us': 2.3706816136837006, 'rows': 46516, 'mb': 2.956312, 'equals_cell_lattice_table': True}} + +--- row cluster_solved --- + [cluster_solved/control] lower 30.6 s compile 22.8 s first 49.20 ms trace_calls 12 step0_rows [276507] + [cluster_solved/exact] lower 29.2 s compile 23.9 s first 22.05 ms trace_calls 12 step0_rows [110085] + [cluster_solved/lattice] lower 29.4 s compile 24.4 s first 12.02 ms trace_calls 12 step0_rows [46516] + [cluster_solved/library] lower 30.0 s compile 24.7 s first 11.43 ms trace_calls 12 step0_rows [46516] + median ms control 47.358 exact 18.815 lattice 9.211 library 9.085 control/lattice 5.141 [5.052, 5.261] + +--- positions gate cluster_solved --- + [cluster_solved_positions/control] lower 5.3 s compile 6.7 s first 50.26 ms trace_calls 2 step0_rows [276507] + [cluster_solved_positions/exact] lower 5.1 s compile 6.9 s first 18.97 ms trace_calls 2 step0_rows [110085] + [cluster_solved_positions/lattice] lower 5.1 s compile 7.1 s first 9.81 ms trace_calls 2 step0_rows [46516] + [cluster_solved_positions/library] lower 5.3 s compile 7.0 s first 9.29 ms trace_calls 2 step0_rows [46516] + +--- row cluster_plain --- + [cluster_plain/control] lower 21.2 s compile 16.7 s first 47.78 ms trace_calls 12 step0_rows [276507] + [cluster_plain/exact] lower 19.8 s compile 18.6 s first 21.69 ms trace_calls 12 step0_rows [110085] + [cluster_plain/lattice] lower 20.2 s compile 18.5 s first 9.62 ms trace_calls 12 step0_rows [46516] + [cluster_plain/library] lower 21.1 s compile 18.7 s first 10.56 ms trace_calls 12 step0_rows [46516] + median ms control 47.604 exact 17.698 lattice 9.243 library 9.225 control/lattice 5.150 [5.044, 5.262] + +============================================================================================================== +STATIC-LATTICE A/B (hpc_ral_cpu_fp64, rounds=20, calls/round=20, constant_folding=off) +============================================================================================================== + row route median p10 p90 min step0 MFLOP temp_MB compile_s + simple_solved control 3.540 3.067 4.296 2.965 69849 7.05 4.82 2.9 + simple_solved exact 2.254 2.084 2.760 2.000 28665 4.06 1.78 3.0 + simple_solved lattice 1.674 1.554 2.143 1.476 11859 3.38 1.69 3.1 + simple_solved library 1.758 1.564 2.144 1.420 11859 3.38 1.69 3.1 + control_over_lattice 2.114 [90% 1.917, 2.156] + control_over_exact 1.570 [90% 1.391, 1.611] + exact_over_lattice 1.346 [90% 1.307, 1.500] + library_over_lattice 1.050 [90% 1.008, 1.214] + control_over_library 2.014 [90% 1.626, 2.091] + MDI 0.347 + simple_solved_vmap4 control 8.258 7.128 9.332 6.964 69849 23.30 15.47 3.4 + simple_solved_vmap4 exact 6.550 5.395 7.564 5.275 28665 14.52 7.09 3.5 + simple_solved_vmap4 lattice 5.761 4.617 6.077 4.472 11859 11.83 6.76 3.7 + simple_solved_vmap4 library 5.784 4.606 6.077 4.483 11859 11.83 6.76 3.7 + control_over_lattice 1.433 [90% 1.421, 1.456] + control_over_exact 1.261 [90% 1.248, 1.279] + exact_over_lattice 1.137 [90% 1.121, 1.158] + library_over_lattice 1.004 [90% 0.993, 1.019] + control_over_library 1.428 [90% 1.417, 1.441] + MDI 0.267 + simple_plain control 3.479 3.047 4.298 2.974 69849 7.05 4.82 2.6 + simple_plain exact 2.267 2.094 2.779 1.979 28665 4.05 1.78 2.7 + simple_plain lattice 1.673 1.550 2.131 1.469 11859 3.38 1.69 2.6 + simple_plain library 1.669 1.512 2.134 1.427 11859 3.38 1.69 2.7 + control_over_lattice 2.080 [90% 1.893, 2.129] + control_over_exact 1.535 [90% 1.296, 1.577] + exact_over_lattice 1.355 [90% 1.320, 1.497] + library_over_lattice 0.998 [90% 0.974, 1.018] + control_over_library 2.084 [90% 1.909, 2.129] + MDI 0.360 + cluster_solved control 47.358 40.514 59.019 36.110 276507 139.44 91.55 22.8 + cluster_solved exact 18.815 16.464 21.133 14.274 110085 64.96 40.44 23.9 + cluster_solved lattice 9.211 7.883 10.345 7.161 46516 39.47 22.64 24.4 + cluster_solved library 9.085 7.890 10.281 7.178 46516 39.47 22.64 24.7 + control_over_lattice 5.141 [90% 5.052, 5.261] + control_over_exact 2.517 [90% 2.481, 2.565] + exact_over_lattice 2.043 [90% 2.013, 2.079] + library_over_lattice 0.986 [90% 0.972, 1.010] + control_over_library 5.213 [90% 5.105, 5.302] + MDI 0.391 + cluster_plain control 47.604 41.116 53.951 36.541 276507 139.43 91.54 16.7 + cluster_plain exact 17.698 15.666 20.097 14.269 110085 64.95 40.43 18.6 + cluster_plain lattice 9.243 8.100 10.228 7.522 46516 39.46 22.63 18.5 + cluster_plain library 9.225 8.025 10.132 7.402 46516 39.46 22.63 18.7 + control_over_lattice 5.150 [90% 5.044, 5.262] + control_over_exact 2.690 [90% 2.629, 2.740] + exact_over_lattice 1.915 [90% 1.893, 1.945] + library_over_lattice 0.998 [90% 0.986, 1.016] + control_over_library 5.160 [90% 5.040, 5.261] + MDI 0.270 +-------------------------------------------------------------------------------------------------------------- + library_matches: lattice + all_gates_pass: True + wall: 637 s + Results JSON: /mnt/ral/jnightin/autolens_profiling_wt/point-source-cpu-p3/results/breakdown/point_source/static_lattice_ab_hpc_ral_cpu_fp64.json + Results PNG: /mnt/ral/jnightin/autolens_profiling_wt/point-source-cpu-p3/results/breakdown/point_source/static_lattice_ab_hpc_ral_cpu_fp64.png +========================================== +run 2/2: constant folding ENABLED, separate process (20 x 20, simple + cluster) +Thu 24 Sep 13:45:37 BST 2026 +loadavg: 3.06 1.68 0.90 1/2096 1665699 +constant folding: requested=ENABLED probe folding_ran=True XLA_FLAGS=--xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false --xla_disable_hlo_passes=constant_folding_probe_disabled + +=== simple: step-0 rows {'control': 69849, 'exact': 28665, 'lattice': 11859}; tables {'cell_build_ms': 82.00608380138874, 'cell_cache_hit_us': 0.5490146577358246, 'exact_table_mb': 1.017432, 'lattice_table_mb': 0.748536, 'library_static_vertex_table': {'build_ms': 34.38778826966882, 'cache_hit_us': 1.2917444109916687, 'rows': 11859, 'mb': 0.748536, 'equals_cell_lattice_table': True}} + +--- row simple_solved --- + [simple_solved/control] lower 2.0 s compile 2.9 s first 7.25 ms trace_calls 2 step0_rows [69849] + [simple_solved/exact] lower 1.9 s compile 2.9 s first 3.19 ms trace_calls 2 step0_rows [28665] + [simple_solved/lattice] lower 1.9 s compile 2.9 s first 3.40 ms trace_calls 2 step0_rows [11859] + [simple_solved/library] lower 1.9 s compile 2.9 s first 3.22 ms trace_calls 2 step0_rows [11859] + median ms control 4.093 exact 2.194 lattice 1.785 library 1.722 control/lattice 2.294 [1.891, 2.389] + +--- positions gate simple_solved --- + [simple_solved_positions/control] lower 1.1 s compile 1.4 s first 5.37 ms trace_calls 1 step0_rows [69849] + [simple_solved_positions/exact] lower 1.0 s compile 1.4 s first 3.08 ms trace_calls 1 step0_rows [28665] + [simple_solved_positions/lattice] lower 1.1 s compile 1.5 s first 2.76 ms trace_calls 1 step0_rows [11859] + [simple_solved_positions/library] lower 1.1 s compile 1.4 s first 3.01 ms trace_calls 1 step0_rows [11859] + +--- gradient gate simple_solved --- + [simple_solved_grad/control] lower 3.4 s compile 4.4 s first 6.67 ms trace_calls 2 step0_rows [69849] + [simple_solved_grad/exact] lower 3.3 s compile 4.6 s first 5.09 ms trace_calls 2 step0_rows [28665] + [simple_solved_grad/lattice] lower 3.3 s compile 4.6 s first 4.15 ms trace_calls 2 step0_rows [11859] + [simple_solved_grad/library] lower 3.3 s compile 4.7 s first 4.25 ms trace_calls 2 step0_rows [11859] + +--- row simple_solved_vmap4 --- + [simple_solved_vmap4/control] lower 2.8 s compile 3.2 s first 9.19 ms trace_calls 2 step0_rows [69849] + [simple_solved_vmap4/exact] lower 2.6 s compile 3.3 s first 7.98 ms trace_calls 2 step0_rows [28665] + [simple_solved_vmap4/lattice] lower 2.6 s compile 3.4 s first 5.91 ms trace_calls 2 step0_rows [11859] + [simple_solved_vmap4/library] lower 2.7 s compile 3.4 s first 5.83 ms trace_calls 2 step0_rows [11859] + median ms control 7.632 exact 5.675 lattice 4.982 library 5.499 control/lattice 1.532 [1.342, 1.651] + +--- row simple_plain --- + [simple_plain/control] lower 1.5 s compile 2.4 s first 4.17 ms trace_calls 2 step0_rows [69849] + [simple_plain/exact] lower 1.4 s compile 2.5 s first 2.78 ms trace_calls 2 step0_rows [28665] + [simple_plain/lattice] lower 1.4 s compile 2.5 s first 2.55 ms trace_calls 2 step0_rows [11859] + [simple_plain/library] lower 1.5 s compile 2.6 s first 3.09 ms trace_calls 2 step0_rows [11859] + median ms control 3.564 exact 2.392 lattice 1.689 library 1.696 control/lattice 2.110 [2.006, 2.313] + +--- step-0 containing_indices gate simple_plain --- + [simple_plain_step0/control] lower 0.2 s compile 0.2 s first 3.29 ms trace_calls 1 step0_rows [69849] + [simple_plain_step0/exact] lower 0.1 s compile 0.2 s first 2.24 ms trace_calls 1 step0_rows [28665] + [simple_plain_step0/lattice] lower 0.1 s compile 0.2 s first 1.91 ms trace_calls 1 step0_rows [11859] + [simple_plain_step0/library] lower 0.2 s compile 0.2 s first 1.97 ms trace_calls 1 step0_rows [11859] + +=== cluster: step-0 rows {'control': 276507, 'exact': 110085, 'lattice': 46516}; tables {'cell_build_ms': 395.868934225291, 'cell_cache_hit_us': 0.5341134965419769, 'exact_table_mb': 3.973416, 'lattice_table_mb': 2.956312, 'library_static_vertex_table': {'build_ms': 181.9877908565104, 'cache_hit_us': 2.1061860024929047, 'rows': 46516, 'mb': 2.956312, 'equals_cell_lattice_table': True}} + +--- row cluster_solved --- + [cluster_solved/control] lower 30.3 s compile 21.9 s first 40.07 ms trace_calls 12 step0_rows [276507] + [cluster_solved/exact] lower 29.2 s compile 24.4 s first 16.39 ms trace_calls 12 step0_rows [110085] + [cluster_solved/lattice] lower 29.1 s compile 23.7 s first 10.46 ms trace_calls 12 step0_rows [46516] + [cluster_solved/library] lower 30.5 s compile 24.1 s first 9.68 ms trace_calls 12 step0_rows [46516] + median ms control 39.026 exact 12.962 lattice 8.506 library 8.599 control/lattice 4.588 [4.444, 4.696] + +--- positions gate cluster_solved --- + [cluster_solved_positions/control] lower 5.1 s compile 6.2 s first 28.69 ms trace_calls 2 step0_rows [276507] + [cluster_solved_positions/exact] lower 5.1 s compile 6.9 s first 17.87 ms trace_calls 2 step0_rows [110085] + [cluster_solved_positions/lattice] lower 5.1 s compile 6.8 s first 11.75 ms trace_calls 2 step0_rows [46516] + [cluster_solved_positions/library] lower 5.4 s compile 6.8 s first 9.41 ms trace_calls 2 step0_rows [46516] + +--- row cluster_plain --- + [cluster_plain/control] lower 21.2 s compile 16.3 s first 34.83 ms trace_calls 12 step0_rows [276507] + [cluster_plain/exact] lower 19.7 s compile 19.4 s first 16.37 ms trace_calls 12 step0_rows [110085] + [cluster_plain/lattice] lower 20.8 s compile 18.6 s first 9.06 ms trace_calls 12 step0_rows [46516] + [cluster_plain/library] lower 21.8 s compile 19.0 s first 10.56 ms trace_calls 12 step0_rows [46516] + median ms control 32.338 exact 13.430 lattice 8.257 library 8.273 control/lattice 3.917 [3.843, 3.975] + +============================================================================================================== +STATIC-LATTICE A/B (hpc_ral_cpu_fp64, rounds=20, calls/round=20, constant_folding=ON) +============================================================================================================== + row route median p10 p90 min step0 MFLOP temp_MB compile_s + simple_solved control 4.093 3.056 4.811 2.957 69849 6.79 4.69 2.9 + simple_solved exact 2.194 2.063 2.773 1.965 28665 3.77 1.77 2.9 + simple_solved lattice 1.785 1.580 2.131 1.464 11859 3.10 1.69 2.9 + simple_solved library 1.722 1.560 2.115 1.462 11859 3.10 1.69 2.9 + control_over_lattice 2.294 [90% 1.891, 2.389] + control_over_exact 1.865 [90% 1.630, 1.908] + exact_over_lattice 1.230 [90% 1.123, 1.284] + library_over_lattice 0.965 [90% 0.875, 1.011] + control_over_library 2.377 [90% 2.077, 2.451] + MDI 0.429 + simple_solved_vmap4 control 7.632 7.099 10.500 6.964 69849 23.02 15.34 3.2 + simple_solved_vmap4 exact 5.675 5.356 7.358 5.227 28665 14.22 7.09 3.3 + simple_solved_vmap4 lattice 4.982 4.600 6.077 4.484 11859 11.53 6.76 3.4 + simple_solved_vmap4 library 5.499 4.607 6.068 4.461 11859 11.53 6.76 3.4 + control_over_lattice 1.532 [90% 1.342, 1.651] + control_over_exact 1.345 [90% 1.307, 1.452] + exact_over_lattice 1.139 [90% 1.002, 1.157] + library_over_lattice 1.104 [90% 0.932, 1.154] + control_over_library 1.388 [90% 1.311, 1.630] + MDI 0.446 + simple_plain control 3.564 3.035 4.800 2.927 69849 6.79 4.69 2.4 + simple_plain exact 2.392 2.046 2.794 1.966 28665 3.77 1.77 2.5 + simple_plain lattice 1.689 1.550 2.122 1.451 11859 3.10 1.69 2.5 + simple_plain library 1.696 1.563 2.131 1.471 11859 3.10 1.69 2.6 + control_over_lattice 2.110 [90% 2.006, 2.313] + control_over_exact 1.490 [90% 1.409, 1.654] + exact_over_lattice 1.416 [90% 1.279, 1.480] + library_over_lattice 1.004 [90% 0.974, 1.041] + control_over_library 2.102 [90% 1.992, 2.254] + MDI 0.495 + cluster_solved control 39.026 30.657 45.269 24.857 276507 131.39 91.88 21.9 + cluster_solved exact 12.962 10.925 15.773 9.752 110085 46.68 40.76 24.4 + cluster_solved lattice 8.506 7.154 9.936 6.303 46516 30.34 22.64 23.7 + cluster_solved library 8.599 7.284 9.889 6.693 46516 30.34 22.64 24.1 + control_over_lattice 4.588 [90% 4.444, 4.696] + control_over_exact 3.011 [90% 2.900, 3.077] + exact_over_lattice 1.524 [90% 1.487, 1.568] + library_over_lattice 1.011 [90% 0.987, 1.037] + control_over_library 4.538 [90% 4.388, 4.640] + MDI 0.374 + cluster_plain control 32.338 27.905 37.959 25.428 276507 131.38 91.54 16.3 + cluster_plain exact 13.430 11.203 15.674 9.895 110085 46.67 40.76 19.4 + cluster_plain lattice 8.257 7.141 9.512 6.295 46516 30.33 22.63 18.6 + cluster_plain library 8.273 7.064 9.580 6.533 46516 30.33 22.63 19.0 + control_over_lattice 3.917 [90% 3.843, 3.975] + control_over_exact 2.408 [90% 2.357, 2.445] + exact_over_lattice 1.627 [90% 1.598, 1.663] + library_over_lattice 1.002 [90% 0.981, 1.020] + control_over_library 3.909 [90% 3.833, 3.988] + MDI 0.311 +-------------------------------------------------------------------------------------------------------------- + library_matches: lattice + all_gates_pass: True + wall: 606 s + Results JSON: /mnt/ral/jnightin/autolens_profiling_wt/point-source-cpu-p3/results/breakdown/point_source/static_lattice_ab_constant_folding_hpc_ral_cpu_fp64.json + Results PNG: /mnt/ral/jnightin/autolens_profiling_wt/point-source-cpu-p3/results/breakdown/point_source/static_lattice_ab_constant_folding_hpc_ral_cpu_fp64.png +========================================== +loadavg: 1.58 1.49 1.17 1/2093 1665794 +Finished. +Thu 24 Sep 13:55:45 BST 2026 diff --git a/results/notes/point_source_cpu_2026_09_24_ral_job_350637_static_lattice_ab_a100.out b/results/notes/point_source_cpu_2026_09_24_ral_job_350637_static_lattice_ab_a100.out new file mode 100644 index 000000000..22ea18745 --- /dev/null +++ b/results/notes/point_source_cpu_2026_09_24_ral_job_350637_static_lattice_ab_a100.out @@ -0,0 +1,166 @@ +AP_ROOT=/mnt/ral/jnightin/autolens_profiling_wt/point-source-cpu-p3 +========================================== +Thu 24 Sep 13:34:42 BST 2026 +euclid-ral-gpu-1 +Model name: AMD EPYC 7702 64-Core Processor +name, driver_version, memory.total [MiB], utilization.gpu [%] +NVIDIA A100 80GB PCIe, 610.57.04, 81920 MiB, 0 % +partition: gpu +gpus: 0 +cpus-per-task: 8 +loadavg: 0.00 0.00 0.00 1/1246 2103922 +Cell: point_source/likelihood_breakdown/static_lattice_ab +Backend: JAX_PLATFORMS=cuda JAX_PLATFORM_NAME=cuda +XLA_FLAGS: --xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false +=== source revisions === +PyAutoNerves (shared) 1fa613aa8d89a79613009fb66f5b4e2d21deb57a +PyAutoFit (shared) a736840127be7a3c21e15a5c945e94d8f1082983 +PyAutoArray (shared) 11b93476b2c7d9d32e5467deedfe9594c637d736 +PyAutoGalaxy (shared) 70a61e26cd715cefe7012a38285698091246a8a4 +PyAutoLens (shared) 2aaa1c1a8bb4a5923edf30a18451fcea330a6bd4 +PyAutoArray (branch clone, IMPORTED): ad0bf97b45d572effa5d428a101d32ac4893b950 +PyAutoLens (branch clone, IMPORTED): b346b6a0a092098f23964e274edb220ea9757ad0 +AP_ROOT rev: 3da2583d46b80f72793caf11a16433ca05f00d20 +AP_ROOT branch: feature/point-source-cpu-p3 +=== import provenance + backend assertion === +autoarray.__file__ = /mnt/ral/jnightin/autolens_profiling_wt/PyAutoArray_point-source-cpu-p3/autoarray/__init__.py +autolens.__file__ = /mnt/ral/jnightin/autolens_profiling_wt/PyAutoLens_point-source-cpu-p3/autolens/__init__.py +autofit.__file__ = /mnt/ral/jnightin/PyAuto/PyAutoFit/autofit/__init__.py +autogalaxy.__file__ = /mnt/ral/jnightin/PyAuto/PyAutoGalaxy/autogalaxy/__init__.py +jax 0.10.2 [CudaDevice(id=0)] gpu +ASSERT OK: GPU backend, autoarray + autolens from the branch clones +========================================== +static-lattice A/B on A100 (20 x 20, simple + cluster) +Thu 24 Sep 13:35:06 BST 2026 +2026-09-24 13:35:10,823 - matplotlib.font_manager - INFO - Failed to extract font properties from /usr/share/fonts/cantarell/Cantarell-VF.otf: In FT2Font: Can not load face (SFNT font table missing; error code 0x8e) +2026-09-24 13:35:10,847 - matplotlib.font_manager - INFO - generated new fontManager +constant folding: requested=disabled probe folding_ran=False XLA_FLAGS=--xla_disable_hlo_passes=constant_folding --xla_gpu_autotune_level=0 --xla_gpu_enable_triton_gemm=false + +=== simple: step-0 rows {'control': 69849, 'exact': 28665, 'lattice': 11859}; tables {'cell_build_ms': 185.3060140274465, 'cell_cache_hit_us': 0.6495974957942963, 'exact_table_mb': 1.017432, 'lattice_table_mb': 0.748536, 'library_static_vertex_table': {'build_ms': 47.864540945738554, 'cache_hit_us': 1.9799917936325073, 'rows': 11859, 'mb': 0.748536, 'equals_cell_lattice_table': True}} + +--- row simple_solved --- + [simple_solved/control] lower 2.7 s compile 5.3 s first 11.40 ms trace_calls 2 step0_rows [69849] + [simple_solved/exact] lower 2.5 s compile 5.4 s first 11.81 ms trace_calls 2 step0_rows [28665] + [simple_solved/lattice] lower 2.5 s compile 5.4 s first 11.25 ms trace_calls 2 step0_rows [11859] + [simple_solved/library] lower 2.6 s compile 5.6 s first 12.73 ms trace_calls 2 step0_rows [11859] + median ms control 0.846 exact 0.843 lattice 0.839 library 0.842 control/lattice 1.009 [1.006, 1.011] + +--- positions gate simple_solved --- + [simple_solved_positions/control] lower 1.4 s compile 4.0 s first 11.46 ms trace_calls 1 step0_rows [69849] + [simple_solved_positions/exact] lower 1.4 s compile 3.8 s first 11.40 ms trace_calls 1 step0_rows [28665] + [simple_solved_positions/lattice] lower 1.3 s compile 3.9 s first 10.34 ms trace_calls 1 step0_rows [11859] + [simple_solved_positions/library] lower 1.4 s compile 3.9 s first 10.18 ms trace_calls 1 step0_rows [11859] + +--- gradient gate simple_solved --- + [simple_solved_grad/control] lower 4.4 s compile 16.7 s first 234.88 ms trace_calls 2 step0_rows [69849] + [simple_solved_grad/exact] lower 4.3 s compile 16.7 s first 21.71 ms trace_calls 2 step0_rows [28665] + [simple_solved_grad/lattice] lower 4.3 s compile 16.9 s first 24.35 ms trace_calls 2 step0_rows [11859] + [simple_solved_grad/library] lower 4.4 s compile 16.6 s first 23.68 ms trace_calls 2 step0_rows [11859] + +--- row simple_solved_vmap4 --- + [simple_solved_vmap4/control] lower 3.6 s compile 6.3 s first 14.38 ms trace_calls 2 step0_rows [69849] + [simple_solved_vmap4/exact] lower 3.4 s compile 6.1 s first 15.02 ms trace_calls 2 step0_rows [28665] + [simple_solved_vmap4/lattice] lower 3.5 s compile 6.2 s first 15.88 ms trace_calls 2 step0_rows [11859] + [simple_solved_vmap4/library] lower 3.6 s compile 6.2 s first 15.82 ms trace_calls 2 step0_rows [11859] + median ms control 0.942 exact 0.922 lattice 0.925 library 0.925 control/lattice 1.019 [1.017, 1.021] + +--- row simple_plain --- + [simple_plain/control] lower 2.0 s compile 5.2 s first 12.80 ms trace_calls 2 step0_rows [69849] + [simple_plain/exact] lower 2.0 s compile 5.2 s first 13.45 ms trace_calls 2 step0_rows [28665] + [simple_plain/lattice] lower 2.0 s compile 5.2 s first 12.03 ms trace_calls 2 step0_rows [11859] + [simple_plain/library] lower 2.0 s compile 5.1 s first 13.01 ms trace_calls 2 step0_rows [11859] + median ms control 0.824 exact 0.821 lattice 0.818 library 0.818 control/lattice 1.008 [1.006, 1.011] + +--- step-0 containing_indices gate simple_plain --- + [simple_plain_step0/control] lower 0.3 s compile 0.5 s first 2.11 ms trace_calls 1 step0_rows [69849] + [simple_plain_step0/exact] lower 0.2 s compile 0.5 s first 2.01 ms trace_calls 1 step0_rows [28665] + [simple_plain_step0/lattice] lower 0.2 s compile 0.5 s first 1.80 ms trace_calls 1 step0_rows [11859] + [simple_plain_step0/library] lower 0.2 s compile 0.5 s first 1.67 ms trace_calls 1 step0_rows [11859] + +=== cluster: step-0 rows {'control': 276507, 'exact': 110085, 'lattice': 46516}; tables {'cell_build_ms': 561.3286113366485, 'cell_cache_hit_us': 0.6100162863731384, 'exact_table_mb': 3.973416, 'lattice_table_mb': 2.956312, 'library_static_vertex_table': {'build_ms': 277.69159572198987, 'cache_hit_us': 2.670101821422577, 'rows': 46516, 'mb': 2.956312, 'equals_cell_lattice_table': True}} + +--- row cluster_solved --- + [cluster_solved/control] lower 40.0 s compile 43.1 s first 44.82 ms trace_calls 12 step0_rows [276507] + [cluster_solved/exact] lower 38.2 s compile 46.3 s first 50.88 ms trace_calls 12 step0_rows [110085] + [cluster_solved/lattice] lower 38.8 s compile 47.6 s first 53.10 ms trace_calls 12 step0_rows [46516] + [cluster_solved/library] lower 41.1 s compile 48.8 s first 56.93 ms trace_calls 12 step0_rows [46516] + median ms control 2.696 exact 2.566 lattice 2.516 library 2.515 control/lattice 1.072 [1.056, 1.085] + +--- positions gate cluster_solved --- + [cluster_solved_positions/control] lower 6.9 s compile 22.6 s first 30.84 ms trace_calls 2 step0_rows [276507] + [cluster_solved_positions/exact] lower 7.0 s compile 23.1 s first 32.41 ms trace_calls 2 step0_rows [110085] + [cluster_solved_positions/lattice] lower 7.0 s compile 23.7 s first 33.49 ms trace_calls 2 step0_rows [46516] + [cluster_solved_positions/library] lower 7.4 s compile 23.5 s first 33.35 ms trace_calls 2 step0_rows [46516] + +--- row cluster_plain --- + [cluster_plain/control] lower 28.9 s compile 36.1 s first 44.01 ms trace_calls 12 step0_rows [276507] + [cluster_plain/exact] lower 26.5 s compile 37.2 s first 41.17 ms trace_calls 12 step0_rows [110085] + [cluster_plain/lattice] lower 27.1 s compile 37.4 s first 43.30 ms trace_calls 12 step0_rows [46516] + [cluster_plain/library] lower 28.7 s compile 38.8 s first 45.49 ms trace_calls 12 step0_rows [46516] + median ms control 2.073 exact 2.002 lattice 1.984 library 1.991 control/lattice 1.045 [1.042, 1.050] + +============================================================================================================== +STATIC-LATTICE A/B (hpc_ral_a100_fp64, rounds=20, calls/round=20, constant_folding=off) +============================================================================================================== + row route median p10 p90 min step0 MFLOP temp_MB compile_s + simple_solved control 0.846 0.836 0.881 0.826 69849 6.60 3.36 5.3 + simple_solved exact 0.843 0.833 0.879 0.823 28665 3.87 0.93 5.4 + simple_solved lattice 0.839 0.829 0.868 0.819 11859 3.07 0.39 5.4 + simple_solved library 0.842 0.832 0.869 0.826 11859 3.07 0.39 5.6 + control_over_lattice 1.009 [90% 1.006, 1.011] + control_over_exact 1.004 [90% 1.001, 1.006] + exact_over_lattice 1.005 [90% 1.003, 1.008] + library_over_lattice 1.003 [90% 1.001, 1.006] + control_over_library 1.005 [90% 1.003, 1.008] + MDI 0.053 + simple_solved_vmap4 control 0.942 0.932 0.974 0.921 69849 21.98 7.83 6.3 + simple_solved_vmap4 exact 0.922 0.910 0.968 0.897 28665 13.64 3.68 6.1 + simple_solved_vmap4 lattice 0.925 0.915 0.954 0.903 11859 10.82 1.53 6.2 + simple_solved_vmap4 library 0.925 0.915 0.957 0.902 11859 10.82 1.53 6.2 + control_over_lattice 1.019 [90% 1.017, 1.021] + control_over_exact 1.022 [90% 1.018, 1.024] + exact_over_lattice 0.997 [90% 0.994, 1.001] + library_over_lattice 1.001 [90% 0.999, 1.003] + control_over_library 1.018 [90% 1.016, 1.020] + MDI 0.045 + simple_plain control 0.824 0.813 0.871 0.804 69849 6.60 3.36 5.2 + simple_plain exact 0.821 0.810 0.872 0.802 28665 3.87 0.93 5.2 + simple_plain lattice 0.818 0.806 0.869 0.796 11859 3.07 0.39 5.2 + simple_plain library 0.818 0.806 0.868 0.796 11859 3.07 0.39 5.1 + control_over_lattice 1.008 [90% 1.006, 1.011] + control_over_exact 1.004 [90% 1.001, 1.006] + exact_over_lattice 1.005 [90% 1.002, 1.008] + library_over_lattice 1.001 [90% 0.998, 1.004] + control_over_library 1.007 [90% 1.005, 1.010] + MDI 0.070 + cluster_solved control 2.696 2.601 2.850 2.557 276507 120.38 42.06 43.1 + cluster_solved exact 2.566 2.447 2.667 2.415 110085 56.77 15.00 46.3 + cluster_solved lattice 2.516 2.428 2.652 2.395 46516 34.59 6.35 47.6 + cluster_solved library 2.515 2.430 2.608 2.392 46516 34.59 6.35 48.8 + control_over_lattice 1.072 [90% 1.056, 1.085] + control_over_exact 1.050 [90% 1.039, 1.062] + exact_over_lattice 1.020 [90% 1.008, 1.028] + library_over_lattice 1.000 [90% 0.988, 1.010] + control_over_library 1.072 [90% 1.058, 1.085] + MDI 0.092 + cluster_plain control 2.073 2.013 2.258 1.955 276507 120.37 42.05 36.1 + cluster_plain exact 2.002 1.951 2.102 1.908 110085 56.76 15.00 37.2 + cluster_plain lattice 1.984 1.937 2.091 1.895 46516 34.57 6.35 37.4 + cluster_plain library 1.991 1.942 2.108 1.887 46516 34.57 6.35 38.8 + control_over_lattice 1.045 [90% 1.042, 1.050] + control_over_exact 1.035 [90% 1.032, 1.040] + exact_over_lattice 1.009 [90% 1.007, 1.013] + library_over_lattice 1.004 [90% 1.001, 1.008] + control_over_library 1.041 [90% 1.037, 1.046] + MDI 0.118 +-------------------------------------------------------------------------------------------------------------- + library_matches: lattice + all_gates_pass: True + wall: 984 s + Results JSON: /mnt/ral/jnightin/autolens_profiling_wt/point-source-cpu-p3/results/breakdown/point_source/static_lattice_ab_hpc_ral_a100_fp64.json + Results PNG: /mnt/ral/jnightin/autolens_profiling_wt/point-source-cpu-p3/results/breakdown/point_source/static_lattice_ab_hpc_ral_a100_fp64.png +========================================== +name, memory.used [MiB] +NVIDIA A100 80GB PCIe, 0 MiB +Finished. +Thu 24 Sep 13:51:32 BST 2026 diff --git a/results/notes/point_source_cpu_campaign.md b/results/notes/point_source_cpu_campaign.md index 60623caec..f8a100cf1 100644 --- a/results/notes/point_source_cpu_campaign.md +++ b/results/notes/point_source_cpu_campaign.md @@ -1,8 +1,8 @@ # Point-source CPU speed-up campaign — ledger Issue: [autolens_profiling #297](https://github.com/PyAutoLabs/autolens_profiling/issues/297) (phase 1); [PyAutoArray #568](https://github.com/PyAutoLabs/PyAutoArray/issues/568) (phase 2) -Branch: `feature/point-source-cpu-p1` (phase 1); `feature/point-source-cpu-p2` (phase 2, PyAutoArray + autolens_profiling) -Status: phase 1 **DONE** (RAL baseline, job 350580); phase 2 **DONE, ACCEPTED** (vertex-dedup A/B, RAL job 350582: 4.47× simple, 1.98× cluster, bit-identical; library change awaiting merge and release); phases 3–4 not started +Branch: `feature/point-source-cpu-p1` (phase 1); `feature/point-source-cpu-p2` (phase 2, PyAutoArray + autolens_profiling); `feature/point-source-cpu-p3` (phase 3, PyAutoArray + PyAutoLens + autolens_profiling) +Status: phase 1 **DONE** (RAL baseline, job 350580); phase 2 **DONE, ACCEPTED** (vertex-dedup A/B, RAL job 350582: 4.47× simple, 1.98× cluster, bit-identical; library change awaiting merge and release); phase 3 **DONE, ACCEPTED** (static step-0 lattice A/B, RAL jobs 350636 CPU / 350637 A100: CPU control → library 2.0–2.1× simple, 1.43× vmap-4, 5.2× cluster, compile +3–12 %, all 31 gates bit-identical; tie-case gate decision pending at ship); phase 4 not started Instrument: [`scripts/point_source/likelihood_breakdown/image_plane.py`](../../scripts/point_source/likelihood_breakdown/image_plane.py) (shipped in #293, see [point_source_shared_likelihood_breakdown.md](point_source_shared_likelihood_breakdown.md)) and [`scripts/cluster/likelihood_breakdown/image_plane.py`](../../scripts/cluster/likelihood_breakdown/image_plane.py) @@ -754,7 +754,7 @@ and `25894d10` (tests), on `11b93476`. --- -## Phase 3 — precompute the static step-0 lattice (2026-09-24) — IN PROGRESS (laptop witness only; RAL pending) +## Phase 3 — precompute the static step-0 lattice (2026-09-24) — DONE, ACCEPTED (RAL jobs 350636 / 350637) Plan: PyAutoArray#568 (re-titled, phase-3 plan comment). Human decisions (2026-09-24): tolerance gate, the geometric 11 859-vertex table, on by default for the JAX PointSolver if the A/B accepts. @@ -820,7 +820,7 @@ control / lattice: ~2×. One earlier folding-on run spent 555 s compiling the cluster-plain control; the rerun took 34 s — a laptop outlier, recorded, not reproduced. -### Correctness gates +### Correctness gates (laptop and library suites) - Laptop, both runs: 31 / 31 gates pass and are **bit-identical** (log L on every streamed instance, solved positions and image counts, `jax.grad` finite and non-zero, vmap-4 vs scalar, simple-plain @@ -830,7 +830,7 @@ control / lattice: traced deflection grid 11 859 rows) is red on main (69 849). - autolens_workspace_test `point_source/jax_likelihood/*.py` ×4 and `jax_grad/gradient.py`: rc 0 on the branch, output identical to main except timing lines; no rtol-1e-4 pin moved. -- **Tie finding (decision for the human at ship).** A source placed *bit-exactly* on a traced step-0 +- **Tie finding (human gate decision pending at ship; see below).** A source placed *bit-exactly* on a traced step-0 lattice vertex changes the step-0 kept set (16 / 50 constructed ties) and, in 1 / 13, the image count: control 3, lattice 2. The control's two positions at the tie vertex (|p − v| = 9.0e-4 each, μ ≈ 4.7) both Newton-converge to the same root, the vertex itself — a duplicate; the lattice path @@ -840,18 +840,192 @@ control / lattice: and in image count on 22 / 25 (lattice vs flat under jit: 6 / 25 and 1 / 25). Generic and near-caustic sources are identical to the flat path. -### Pending +### RAL environment (quotable) + +- **CPU — job 350636**, partition `ral`, pinned `--nodelist=euclid-ral-compute-10-2` (Intel Xeon + Platinum 8490H, the phase-1 host; phase 2 ran on an EPYC 7763, so only in-job ratios compare across + phases). 8 CPUs (`sched_affinity` 8, `NPROC=8`, BLAS threads 1), fp64, JAX 0.10.2, loadavg 0.28 at + start. Two runs in one job: folding **off** (the PyAutoNerves default) then folding **on** + (`--constant-folding`; the HLO probe confirms folding ran, `agrees_with_request: true`). Walls 637 s + and 606 s. Empty stderr. +- **A100 — job 350637**, `euclid-ral-gpu-1`, one NVIDIA A100 80GB PCIe, backend asserted `gpu`, fp64, + folding off, wall 984 s. Empty stderr. +- **Provenance (all three JSONs):** `source_revisions` PyAutoArray `ad0bf97b`, PyAutoLens `b346b6a0` + (imported from the branch clones `/mnt/ral/jnightin/autolens_profiling_wt/{PyAutoArray,PyAutoLens}_point-source-cpu-p3`, + refused otherwise), autolens_profiling `3da2583`, PyAutoGalaxy `70a61e26`, PyAutoFit `a7368401`, + PyAutoNerves `1fa613aa`. `library_matches: lattice` on every row; on the simple rows the library's + optimised HLO is hash-identical to the cell's lattice route. `all_gates_pass: true`. +- Protocol as the laptop: 20 rounds × 20 calls (400 samples per route), round-robin with rotated + start, 3 warm calls, 16-instance parameter stream. The library `static_vertex_table` equals the cell's + lattice table on both geometries; build 34 ms / 0.75 MB (simple) and 184 ms / 2.96 MB (cluster) on the + Xeon, cache hit 1.2–2.4 µs. + +Numbers below are read from the JSONs by script. Ratios are per-route medians with the JSON's bootstrap +90 % CI; **MDI** = control (p90 − p10) / median; compile is `compile_s` of a **single cold compile per +route** (not a median); temp = XLA `memory_analysis().temp_size_in_bytes`; FLOPs = `cost_analysis()`. + +**RAL CPU, constant folding off** (`static_lattice_ab_hpc_ral_cpu_fp64.json`) + +| Row | control median [p10, p90] ms | exact | lattice | library | control / library [90 % CI] | control / lattice [90 % CI] | paired per-round c/lat median [p10, p90] | MDI | compile s ctrl → lib | temp MB ctrl → lib | FLOPs ctrl → lib | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| `simple_solved` | 3.540 [3.067, 4.296] | 2.254 | 1.674 | 1.758 | **2.01×** [1.63, 2.09] | 2.11× [1.92, 2.16] | 1.94 [1.79, 2.13] | 34.7 % | 2.91 → 3.14 (+8.0 %) | 4.82 → 1.69 | 7.05M → 3.38M | +| `simple_solved_vmap4` (per batch of 4) | 8.258 [7.128, 9.332] | 6.550 | 5.761 | 5.784 | **1.43×** [1.42, 1.44] | 1.43× [1.42, 1.46] | 1.44 [1.39, 1.63] | 26.7 % | 3.39 → 3.71 (+9.5 %) | 15.47 → 6.76 | 23.30M → 11.83M | +| `simple_plain` | 3.479 [3.047, 4.298] | 2.267 | 1.673 | 1.669 | **2.08×** [1.91, 2.13] | 2.08× [1.89, 2.13] | 1.92 [1.72, 2.17] | 36.0 % | 2.58 → 2.67 (+3.3 %) | 4.82 → 1.69 | 7.05M → 3.38M | +| `cluster_solved` | 47.36 [40.51, 59.02] | 18.82 | 9.211 | 9.085 | **5.21×** [5.10, 5.30] | 5.14× [5.05, 5.26] | 5.20 [4.91, 5.44] | 39.1 % | 22.77 → 24.66 (+8.3 %) | 91.55 → 22.64 | 139.44M → 39.47M | +| `cluster_plain` | 47.60 [41.12, 53.95] | 17.70 | 9.243 | 9.225 | **5.16×** [5.04, 5.26] | 5.15× [5.04, 5.26] | 5.16 [4.85, 5.59] | 27.0 % | 16.69 → 18.72 (+12.2 %) | 91.54 → 22.63 | 139.43M → 39.46M | + +**RAL CPU, constant folding on** (`static_lattice_ab_constant_folding_hpc_ral_cpu_fp64.json`) + +| Row | control median [p10, p90] ms | exact | lattice | library | control / library [90 % CI] | control / lattice [90 % CI] | paired per-round c/lat median [p10, p90] | MDI | compile s ctrl → lib | temp MB ctrl → lib | FLOPs ctrl → lib | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| `simple_solved` | 4.093 [3.056, 4.811] | 2.194 | 1.785 | 1.722 | **2.38×** [2.08, 2.45] | 2.29× [1.89, 2.39] | 2.12 [1.72, 2.34] | 42.9 % | 2.85 → 2.91 (+1.9 %) | 4.69 → 1.69 | 6.79M → 3.10M | +| `simple_solved_vmap4` (per batch of 4) | 7.632 [7.099, 10.500] | 5.675 | 4.982 | 5.499 | **1.39×** [1.31, 1.63] | 1.53× [1.34, 1.65] | 1.55 [1.29, 1.72] | 44.6 % | 3.22 → 3.43 (+6.4 %) | 15.34 → 6.76 | 23.02M → 11.53M | +| `simple_plain` | 3.564 [3.035, 4.800] | 2.392 | 1.689 | 1.696 | **2.10×** [1.99, 2.25] | 2.11× [2.01, 2.31] | 2.03 [1.81, 2.40] | 49.5 % | 2.44 → 2.57 (+5.2 %) | 4.69 → 1.69 | 6.79M → 3.10M | +| `cluster_solved` | 39.03 [30.66, 45.27] | 12.96 | 8.506 | 8.599 | **4.54×** [4.39, 4.64] | 4.59× [4.44, 4.70] | 4.58 [4.19, 4.94] | 37.4 % | 21.93 → 24.14 (+10.1 %) | 91.88 → 22.64 | 131.39M → 30.34M | +| `cluster_plain` | 32.34 [27.91, 37.96] | 13.43 | 8.257 | 8.273 | **3.91×** [3.83, 3.99] | 3.92× [3.84, 3.98] | 3.91 [3.58, 4.22] | 31.1 % | 16.28 → 18.97 (+16.5 %) | 91.54 → 22.63 | 131.38M → 30.33M | + +**RAL A100, fp64** (`static_lattice_ab_hpc_ral_a100_fp64.json`) + +| Row | control median [p10, p90] ms | exact | lattice | library | control / library [90 % CI] | control / lattice [90 % CI] | MDI | compile s ctrl → lib | temp MB ctrl → lib | FLOPs ctrl → lib | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| `simple_solved` | 0.846 [0.836, 0.881] | 0.843 | 0.839 | 0.842 | **1.01×** [1.00, 1.01] | 1.01× [1.01, 1.01] | 5.3 % | 5.28 → 5.57 (+5.4 %) | 3.36 → 0.39 | 6.60M → 3.07M | +| `simple_solved_vmap4` (per batch of 4) | 0.942 [0.932, 0.974] | 0.922 | 0.925 | 0.925 | **1.02×** [1.02, 1.02] | 1.02× [1.02, 1.02] | 4.5 % | 6.28 → 6.19 (−1.5 %) | 7.83 → 1.53 | 21.98M → 10.82M | +| `simple_plain` | 0.824 [0.813, 0.871] | 0.821 | 0.818 | 0.818 | **1.01×** [1.00, 1.01] | 1.01× [1.01, 1.01] | 7.0 % | 5.19 → 5.10 (−1.6 %) | 3.36 → 0.39 | 6.60M → 3.07M | +| `cluster_solved` | 2.696 [2.601, 2.850] | 2.566 | 2.516 | 2.515 | **1.07×** [1.06, 1.09] | 1.07× [1.06, 1.08] | 9.2 % | 43.13 → 48.84 (+13.2 %) | 42.06 → 6.35 | 120.38M → 34.59M | +| `cluster_plain` | 2.073 [2.013, 2.258] | 2.002 | 1.984 | 1.991 | **1.04×** [1.04, 1.05] | 1.05× [1.04, 1.05] | 11.8 % | 36.11 → 38.76 (+7.4 %) | 42.05 → 6.35 | 120.37M → 34.57M | + +**Reading the rows.** + +- **CPU gain is material and far outside the noise.** Folding off, the library is 2.0–2.1× faster on the + simple likelihood (3.54 → 1.76 ms solved, 3.48 → 1.67 ms plain) and **5.2×** on the two-source cluster + (47.4 → 9.1 ms), with every CI and every paired per-round p10 above 1.6 (simple scalar rows) and 4.8 (cluster). + `library / lattice` is 0.99–1.05 (CIs straddle or touch 1): the shipped code reproduces the cell's + lattice route. The cluster gains more because its 276 507-row step-0 input shrinks 5.9× to 46 516 + rows and its 13-component deflections dominate the call. +- **`exact` (28 665 / 110 085 rows) captures about half the gain**; the geometric dedup to 11 859 / + 46 516 rows is the other half (exact / lattice 1.35× simple, 2.0× cluster). The human decision to + ship the geometric table is borne out. +- **`vmap`-4 gains less (1.43×)**, as in phase 2: the step-0 lattice is batch-invariant, so under `vmap` + the control already amortises its step-0 work across the batch. Per likelihood the library costs + 1.45 ms at batch 4 against 1.76 ms scalar. +- **Folding on does not change the verdict.** It speeds the control's cluster rows (47.4 → 39.0 / + 32.3 ms) because the flat step-0 table becomes a folded constant; the library is barely affected + (9.1 → 8.6 / 8.3 ms). The library still wins 2.1–2.4× simple, 1.39× vmap-4 and 3.9–4.5× cluster. + Folding also trims FLOPs (library cluster 39.5M → 30.3M). Folding stays off by default (PyAutoNerves); + nothing here argues for flipping it. +- **Compile (the open laptop question) — resolved on the quiet node.** Control → library `compile_s` + is **+3.3 % to +12.2 %** folding off and **+1.9 % to +16.5 %** folding on (worst: cluster plain 16.28 → + 18.97 s); lowering is unchanged or faster (cluster solved 30.58 → 30.03 s) and first call falls + (cluster solved 49 → 11 ms). Every row is under the +20 % stop rule. The laptop's readings (+37 % and + +63 % on some rows, −21 % on another) did not reproduce: they were load noise on single samples. Caveat: each figure is one cold + compile per route, not a median, so differences of a few percent are not resolved. +- **Memory — no regression, large reduction.** XLA temp 4.82 → 1.69 MB (simple), 91.6 → 22.6 MB + (cluster), 15.5 → 6.8 MB (vmap-4); on the A100 3.36 → 0.39 MB and 42.1 → 6.35 MB. RSS delta at compile + falls on every heavy row (cluster solved 861 → 82 MB CPU, 907 → 178 MB A100). The table itself is a + 0.75 / 2.96 MB host constant, built once per geometry. +- **A100 — no regression, a small gain.** The GPU call is launch/latency-bound, not FLOP-bound: + halving (simple) or cutting 3.5× (cluster) the FLOPs moves the call by only 1–7 % (0.846 → 0.842 ms, + 2.70 → 2.52 ms). All CIs are ≥ 1.00. Compile is −1.6 % to +13.2 % (cluster solved 43.1 → 48.8 s), under + +20 %. +- **MDI 27–50 % on the Xeon node is large**: it is per-call dispersion (p10–p90 across a 16-instance + parameter stream and 400 calls), not uncertainty of the median. The effects are 2–5×, and the + bootstrap CIs of the ratios are a few percent wide. + +### Correctness gates (RAL) + +All **31 / 31** gates in `gate_summary` pass in all three JSONs, with max |Δ| = 0 everywhere: + +- log L on all five rows, every route pair (control vs exact / lattice / library), all 16 stream + instances, **bit-identical** (NaN pattern equal), deterministic within each route; fiducial simple + solved `7.743201200876812` on every route (identical to phases 1–2); +- solved positions and image counts: simple 4 finite per instance, cluster 3 + 3, three instances each, + padded arrays equal, max |Δ| = 0.0; +- simple-solved `jax.grad`: finite, non-zero on every instance, identical between routes (Δ = 0); +- vmap-4 against scalar within each route and across routes: bit-identical; +- simple-plain step-0 `containing_indices` sets identical across routes. + +The same holds on CPU with folding on and on the A100. + +### Tie finding — human gate decision pending at ship + +Not exercised by the RAL gates (stream sources are generic). From the laptop study above: a source +placed **bit-exactly on a traced step-0 lattice vertex** can change the step-0 kept set (16 / 50 +constructed ties) and, in 1 / 13, the image count — **control 3 vs lattice 2**. The control's third image +is a **duplicate**: its two positions at the tie vertex both Newton-converge to the same root, the vertex +itself; the lattice path returns exactly the system's 2 true images. Main's own flat path is **not +self-consistent** at such ties: eager vs jit differ in image count on **22 / 25** (lattice vs flat under +jit: 1 / 25). Nudging the source by 1e-9 gives 2 images on both paths. The plan's gate asks for +"identical image counts" and so is formally failed on this constructed measure-zero case; accepting it +(as a duplicate-removal, not a lost image) is a **human decision recorded at ship**. + +### Decision — ACCEPT (pending the tie-case sign-off) + +Stop rule (PyAutoArray #568): reject if the simple gain < max(5 %, 2× MDI) with no cluster gain; any +gate fails; compile +20 %; memory or GPU regression. + +- **Gain.** Simple solved speed-up +101 % (2.01×) against 2× MDI = 69 %; cluster +421 % (5.21×) against + 78 %. The cluster clause alone rules out rejection. (Read as a fractional time reduction instead — + 50 % simple vs 69 % — the simple row alone would not clear 2× MDI on this high-dispersion node, but the + cluster's 81 % reduction does, and the paired per-round p10 is 1.79× simple / 4.91× cluster.) +- **Gates.** 31 / 31 bit-identical on CPU (both folding modes) and A100; the tie case above is the only + open item. +- **Compile.** Worst +12.2 % (folding off), +16.5 % (folding on), +13.2 % (A100): all < +20 %. +- **Memory / GPU.** XLA temp memory −56 % to −88 %; A100 1.01–1.07× faster. + +Ship library-first (PyAutoArray `ad0bf97b` → PyAutoLens `b346b6a0` → autolens_profiling), on by default +for the JAX PointSolver as decided on 2026-09-24. + +### Post-fix budget and phase-4 handoff + +After phase 3 the step-0 lattice is no longer the dominant cost on `simple`. FLOP accounting (folding +off, hardware-independent; the step-0 share is an **estimate** from the marginal FLOP per removed step-0 +row between the control and lattice routes): + +- **Simple (3.38M FLOP).** The marginal step-0 cost is (7.05 − 3.38)M / (69 849 − 11 859) ≈ 63 FLOP per + row, so the 11 859-row step 0 is ≈ 0.75M (**≈ 22 %**). The seven refinement steps' deflections + (7 × 289 599 = 2.03M, phase 2) are now **≈ 60 %**; containment, the `f64[60]` neighbourhood sorts, β\*, + the magnification filter and χ² share the remaining ≈ 0.6M (≈ 18 %). +- **Cluster (39.47M FLOP).** ≈ 435 FLOP per step-0 row → the 46 516-row step 0 is ≈ 20M (**≈ 51 %**); + the refinement steps and the rest are ≈ 19M. Deflections of the 13-component lens still dominate + both halves. +- **GPU.** The A100 call barely moved with FLOPs, so FLOP levers will not show on GPU; any GPU + work is a launch-count/latency question, separately scoped. + +**Re-ranked phase-4 levers (proposed, none measured):** + +1. **Simple: initial scale versus refinement steps (old 4(b)).** The seven refinement steps now carry + ≈ 60 % of the simple call; fewer steps at a finer step 0 (or a coarser final precision where + admissible) is the largest remaining lever. It is a correctness knob: image completeness and + position precision across a prior must be shown. +2. **Cluster: dPIE/NFW deflection cost (old 4(d)).** Deflections dominate every cluster step; this is + a separately scoped PyAutoGalaxy phase. +3. **Grid-extent guidance (old 4(a)).** Now acts on the ≈ 22 % (simple) / ≈ 51 % (cluster) step-0 + share only, plus containment; still needs image-completeness evidence. Stronger at cluster scale. +4. **`MAX_CONTAINING_SIZE` / neighbourhood fan-out (old 4(c)).** Sets the 720-point refinement grids and + the `f64[60]` sorts, so it now scales the dominant simple share; correctness knob. + +Before ranking by wall time, run the phase-1 breakdown cells on the post-phase-3 code (after release) +so the per-step time split, not only FLOPs, backs the choice. + +### Reproduce (RAL) + +```bash +# RAL worktree of feature/point-source-cpu-p3, with the PyAutoArray / PyAutoLens branch clones beside it +cd hpc/batch_cpu && sbatch submit_breakdown_point_source_static_lattice_ab_ral_cpu_fp64 # folding off, then on +cd ../batch_gpu && sbatch submit_breakdown_point_source_static_lattice_ab_a100_fp64 +``` -- RAL CPU job (`hpc/batch_cpu/submit_breakdown_point_source_static_lattice_ab_ral_cpu_fp64`, folding - off then on, pinned to `euclid-ral-compute-10-2` — the phase-1 Xeon 8490H host, because - `euclid-ral-compute-1..19` were not responding on 2026-09-24) and the A100 leg - (`hpc/batch_gpu/submit_breakdown_point_source_static_lattice_ab_a100_fp64`), both importing branch - clones of PyAutoArray `ad0bf97b` / PyAutoLens `b346b6a0`. Accept or reject by the stop rule on - those numbers: gain ≥ max(5 %, 2× MDI), compile ≤ +20 % (simple and cluster), no memory or GPU - regression, all gates. +JSONs and PNGs: [`static_lattice_ab_hpc_ral_cpu_fp64`](../breakdown/point_source/static_lattice_ab_hpc_ral_cpu_fp64.json) +([png](../breakdown/point_source/static_lattice_ab_hpc_ral_cpu_fp64.png)), +[`static_lattice_ab_constant_folding_hpc_ral_cpu_fp64`](../breakdown/point_source/static_lattice_ab_constant_folding_hpc_ral_cpu_fp64.json) +([png](../breakdown/point_source/static_lattice_ab_constant_folding_hpc_ral_cpu_fp64.png)), +[`static_lattice_ab_hpc_ral_a100_fp64`](../breakdown/point_source/static_lattice_ab_hpc_ral_a100_fp64.json) +([png](../breakdown/point_source/static_lattice_ab_hpc_ral_a100_fp64.png)). +Logs: [`point_source_cpu_2026_09_24_ral_job_350636_static_lattice_ab.out`](point_source_cpu_2026_09_24_ral_job_350636_static_lattice_ab.out), +[`point_source_cpu_2026_09_24_ral_job_350637_static_lattice_ab_a100.out`](point_source_cpu_2026_09_24_ral_job_350637_static_lattice_ab_a100.out). ## Phase 4 — profile the residue and iterate — NOT STARTED -Rank by new evidence: (a) grid-extent guidance, (b) initial scale versus -refinement count, (c) `MAX_CONTAINING_SIZE` / neighbourhood fan-out and -(d) cluster dPIE/NFW deflections. Any PyAutoGalaxy work is its own phase. +Rank by new evidence; the phase-3 re-rank above puts (b) initial scale versus refinement count +first for `simple` and (d) cluster dPIE/NFW deflections first at cluster scale, then (a) grid-extent +guidance and (c) `MAX_CONTAINING_SIZE` / neighbourhood fan-out. Any PyAutoGalaxy work is its own phase. From 9055b3b101696a49e04c2d89380854dbad8b3a4a Mon Sep 17 00:00:00 2001 From: Jammy2211 Date: Thu, 24 Sep 2026 15:17:48 +0200 Subject: [PATCH 5/6] notes(point_source): phase-3 tie case PASSED by human decision 2026-09-24, pinned as a PyAutoLens test The source-on-step-0-vertex tie (control 3 images incl. a duplicate root vs lattice 2) is accepted as duplicate removal and pinned as test__source_on_a_step_0_vertex_returns_the_two_true_images (PyAutoLens feature/point-source-cpu-p3 972d454e). README compile figures made exact. Co-Authored-By: Claude Opus 5.5 --- README.md | 6 ++--- results/notes/point_source_cpu_campaign.md | 28 ++++++++++++++-------- 2 files changed, 21 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 824fddd00..a407d8bf5 100644 --- a/README.md +++ b/README.md @@ -269,10 +269,10 @@ separately per likelihood × transform. Standing conclusions: (46 516 vs 276 507 on the cluster; PyAutoArray #568, PyAutoLens). Four-route in-process A/B `static_lattice_ab.py`, RAL job 350636 (`ral`, pinned Xeon 8490H, 8 CPUs, folding off): simple solved 3.54 → 1.76 ms (**2.01×**), vmap-4 1.43×, two-source cluster 47.4 → 9.1 ms (**5.21×**); FLOPs −52 % / - −72 %, XLA temp 4.8 → 1.7 MB / 91.6 → 22.6 MB, compile +3–12 % (+17 % worst with constant folding on, + −72 %, XLA temp 4.8 → 1.7 MB / 91.6 → 22.6 MB, compile +3.3–12.2 % (+16.5 % worst with constant folding on, which gives 2.1–4.5× on the scalar rows). A100 (job 350637): no regression, 1.01–1.07× faster. All 31 gates bit-identical. - Accepted; a bit-exact source-on-vertex tie (control 3 images incl. a duplicate root vs lattice 2) is a - human gate decision at ship. Post-fix, the refinement-step deflections (≈ 60 % of simple FLOPs) lead phase 4. + Accepted; a bit-exact source-on-vertex tie (control 3 images incl. a duplicate root vs lattice 2) PASSED by + human decision 2026-09-24 and is pinned as a PyAutoLens test. Post-fix, the refinement-step deflections (≈ 60 % of simple FLOPs) lead phase 4. Findings: [`results/notes/point_source_cpu_campaign.md`](./results/notes/point_source_cpu_campaign.md). ## How to read this repo diff --git a/results/notes/point_source_cpu_campaign.md b/results/notes/point_source_cpu_campaign.md index f8a100cf1..b1f0fd498 100644 --- a/results/notes/point_source_cpu_campaign.md +++ b/results/notes/point_source_cpu_campaign.md @@ -2,7 +2,7 @@ Issue: [autolens_profiling #297](https://github.com/PyAutoLabs/autolens_profiling/issues/297) (phase 1); [PyAutoArray #568](https://github.com/PyAutoLabs/PyAutoArray/issues/568) (phase 2) Branch: `feature/point-source-cpu-p1` (phase 1); `feature/point-source-cpu-p2` (phase 2, PyAutoArray + autolens_profiling); `feature/point-source-cpu-p3` (phase 3, PyAutoArray + PyAutoLens + autolens_profiling) -Status: phase 1 **DONE** (RAL baseline, job 350580); phase 2 **DONE, ACCEPTED** (vertex-dedup A/B, RAL job 350582: 4.47× simple, 1.98× cluster, bit-identical; library change awaiting merge and release); phase 3 **DONE, ACCEPTED** (static step-0 lattice A/B, RAL jobs 350636 CPU / 350637 A100: CPU control → library 2.0–2.1× simple, 1.43× vmap-4, 5.2× cluster, compile +3–12 %, all 31 gates bit-identical; tie-case gate decision pending at ship); phase 4 not started +Status: phase 1 **DONE** (RAL baseline, job 350580); phase 2 **DONE, ACCEPTED** (vertex-dedup A/B, RAL job 350582: 4.47× simple, 1.98× cluster, bit-identical; library change awaiting merge and release); phase 3 **DONE, ACCEPTED** (static step-0 lattice A/B, RAL jobs 350636 CPU / 350637 A100: CPU control → library 2.0–2.1× simple, 1.43× vmap-4, 5.2× cluster, compile +3–12 %, all 31 gates bit-identical; source-on-vertex tie case PASSED by human decision 2026-09-24, pinned as PyAutoLens `test__source_on_a_step_0_vertex_returns_the_two_true_images`); phase 4 not started Instrument: [`scripts/point_source/likelihood_breakdown/image_plane.py`](../../scripts/point_source/likelihood_breakdown/image_plane.py) (shipped in #293, see [point_source_shared_likelihood_breakdown.md](point_source_shared_likelihood_breakdown.md)) and [`scripts/cluster/likelihood_breakdown/image_plane.py`](../../scripts/cluster/likelihood_breakdown/image_plane.py) @@ -825,12 +825,12 @@ control / lattice: - Laptop, both runs: 31 / 31 gates pass and are **bit-identical** (log L on every streamed instance, solved positions and image counts, `jax.grad` finite and non-zero, vmap-4 vs scalar, simple-plain step-0 `containing_indices` sets). -- PyAutoArray suite 1645 passed (+13 static-table tests); PyAutoLens suite 755 passed, 1 xfailed - (+15 JAX tests in `test_static_lattice_jax.py`, which skip without jax). The shape guard (first +- PyAutoArray suite 1645 passed (+13 static-table tests); PyAutoLens suite 756 passed, 1 xfailed + (+16 JAX tests in `test_static_lattice_jax.py`, incl. the pinned tie case, which skip without jax). The shape guard (first traced deflection grid 11 859 rows) is red on main (69 849). - autolens_workspace_test `point_source/jax_likelihood/*.py` ×4 and `jax_grad/gradient.py`: rc 0 on the branch, output identical to main except timing lines; no rtol-1e-4 pin moved. -- **Tie finding (human gate decision pending at ship; see below).** A source placed *bit-exactly* on a traced step-0 +- **Tie finding (PASSED by human decision 2026-09-24; see below).** A source placed *bit-exactly* on a traced step-0 lattice vertex changes the step-0 kept set (16 / 50 constructed ties) and, in 1 / 13, the image count: control 3, lattice 2. The control's two positions at the tie vertex (|p − v| = 9.0e-4 each, μ ≈ 4.7) both Newton-converge to the same root, the vertex itself — a duplicate; the lattice path @@ -946,7 +946,7 @@ All **31 / 31** gates in `gate_summary` pass in all three JSONs, with max |Δ| = The same holds on CPU with folding on and on the A100. -### Tie finding — human gate decision pending at ship +### Tie finding — PASSED by human decision 2026-09-24 Not exercised by the RAL gates (stream sources are generic). From the laptop study above: a source placed **bit-exactly on a traced step-0 lattice vertex** can change the step-0 kept set (16 / 50 @@ -956,9 +956,17 @@ itself; the lattice path returns exactly the system's 2 true images. Main's own self-consistent** at such ties: eager vs jit differ in image count on **22 / 25** (lattice vs flat under jit: 1 / 25). Nudging the source by 1e-9 gives 2 images on both paths. The plan's gate asks for "identical image counts" and so is formally failed on this constructed measure-zero case; accepting it -(as a duplicate-removal, not a lost image) is a **human decision recorded at ship**. +(as a duplicate-removal, not a lost image) was put to the human at ship. -### Decision — ACCEPT (pending the tie-case sign-off) +**Decision (human, 2026-09-24): PASS.** The case is accepted as the removal of a duplicate and pinned +as a regression test, PyAutoLens `test_autolens/point/triangles/test_static_lattice_jax.py::test__source_on_a_step_0_vertex_returns_the_two_true_images` +(branch `feature/point-source-cpu-p3` `972d454e`): the simple SIE of the cell, source on the image of +step-0 vertex v = (−0.8, −1.99185843) (flat vertex 31 732), solved under `jit` with the source a traced +input; the static-lattice solver must return exactly the 2 true images (v and the counter-image +~(0.41013634, 0.89463539)) within 2 × `pixel_scale_precision`. With the source a closed-over constant +instead, the flat path also returns 2 here — one more face of the flat path's instability at ties. + +### Decision — ACCEPT Stop rule (PyAutoArray #568): reject if the simple gain < max(5 %, 2× MDI) with no cluster gain; any gate fails; compile +20 %; memory or GPU regression. @@ -967,12 +975,12 @@ gate fails; compile +20 %; memory or GPU regression. 78 %. The cluster clause alone rules out rejection. (Read as a fractional time reduction instead — 50 % simple vs 69 % — the simple row alone would not clear 2× MDI on this high-dispersion node, but the cluster's 81 % reduction does, and the paired per-round p10 is 1.79× simple / 4.91× cluster.) -- **Gates.** 31 / 31 bit-identical on CPU (both folding modes) and A100; the tie case above is the only - open item. +- **Gates.** 31 / 31 bit-identical on CPU (both folding modes) and A100; the tie case above PASSED by + human decision 2026-09-24 and is pinned as a PyAutoLens test. - **Compile.** Worst +12.2 % (folding off), +16.5 % (folding on), +13.2 % (A100): all < +20 %. - **Memory / GPU.** XLA temp memory −56 % to −88 %; A100 1.01–1.07× faster. -Ship library-first (PyAutoArray `ad0bf97b` → PyAutoLens `b346b6a0` → autolens_profiling), on by default +Ship library-first (PyAutoArray `ad0bf97b` → PyAutoLens `b346b6a0` + tie test `972d454e` → autolens_profiling), on by default for the JAX PointSolver as decided on 2026-09-24. ### Post-fix budget and phase-4 handoff From 629ce0c1b1cf92d2eb6bc6a8fc3ec576f4505657 Mon Sep 17 00:00:00 2001 From: Jammy2211 Date: Thu, 24 Sep 2026 15:20:10 +0200 Subject: [PATCH 6/6] notes(point_source): phase-3 PyAutoArray test count is +19 cases (10 -> 29) Co-Authored-By: Claude Opus 5.5 --- results/notes/point_source_cpu_campaign.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/results/notes/point_source_cpu_campaign.md b/results/notes/point_source_cpu_campaign.md index b1f0fd498..a5f8b6acb 100644 --- a/results/notes/point_source_cpu_campaign.md +++ b/results/notes/point_source_cpu_campaign.md @@ -825,7 +825,7 @@ control / lattice: - Laptop, both runs: 31 / 31 gates pass and are **bit-identical** (log L on every streamed instance, solved positions and image counts, `jax.grad` finite and non-zero, vmap-4 vs scalar, simple-plain step-0 `containing_indices` sets). -- PyAutoArray suite 1645 passed (+13 static-table tests); PyAutoLens suite 756 passed, 1 xfailed +- PyAutoArray suite 1645 passed (+19 static-table test cases); PyAutoLens suite 756 passed, 1 xfailed (+16 JAX tests in `test_static_lattice_jax.py`, incl. the pinned tie case, which skip without jax). The shape guard (first traced deflection grid 11 859 rows) is red on main (69 849). - autolens_workspace_test `point_source/jax_likelihood/*.py` ×4 and `jax_grad/gradient.py`: rc 0 on